Heterogeneous Smart Research Articles

Optimal search mapping among sensors in heterogeneous smart homes.

There are huge differences in the layouts and numbers of sensors in different smart home environments. Daily activities performed by residents trigger a variety of sensor event streams. Solving the problem of sensor mapping is an important prerequisite for the transfer of activity features in smart homes. However, it is common practice among most of the existing approaches that only sensor profile information or the ontological relationship between sensor location and furniture attachment are used for sensor mapping. The rough mapping seriously restricts the performance of daily activity recognition. This paper presents a mapping approach based on the optimal search for sensors. To begin with, a source smart home that is similar to the target one is selected. Thereafter, sensors in both source and target smart homes are grouped by sensor profile information. In addition, sensor mapping space is built. Furthermore, a small amount of data collected from the target smart home is used to evaluate each instance in sensor mapping space. In conclusion, Deep Adversarial Transfer Network is employed to perform daily activity recognition among heterogeneous smart homes. Testing is conducted using the public CASAC data set. The results have revealed that the proposed approach achieves a 7-10% improvement in accuracy, 5-11% improvement in precision, and 6-11% improvement in F1 score, compared with the existing methods.

A Semantic MSOAH-IoT Design for Improving Efficiency and Solving Heterogeneity within IoT Applications

The integration of Internet of Things (IoT) technology in order to develop and manage different real life applications such as smart cities, environmental surveillance, health care, precision agriculture and traffic monitoring, has proven to be a game changer. Different IoT hardware and software solutions are merging to meet different application requirements. However, with each new IoT application, there are new challenges to solve, because of the variety of communication protocols, data formats and the heterogeneity of smart objects on the network. Many companies develop middleware platforms to overcome the heterogeneity problems. However, most of these solutions are proprietary, monopolized and developed as vertical proprietary application stacks, so it prevents developers from reusing modules. This paper demonstrates some recent middleware approaches, especially the Service Oriented Architecture for Heterogeneity Issues within the Internet of Things (MSOAH-IoT), designed to solve hardware and software heterogeneity issues within IoT applications. Then proposes an enhancement of the MSOAH-IoT middleware, by implementing new features such as semantic web and context awareness, in order to establish a smart gateway, able to make decisions for automated management, while ensuring connection between heterogeneous smart objects.

Bibliometric review of actuators: Key automation technology in a smart city framework

Smart cities are built in diverse mechanisms and sub-systems by integrating Information and Communication Technologies (ICT). It offers inhabitants sustainable services at a lower cost and provides full connectivity. The sensors are incorporated virtually everywhere in a smart city environment. Actuators are switching technologies that enable communication in a highly heterogeneous smart city environment and perform output functions. This article focuses on the function of actuators as a vital technology to allow different smart-city services to be automated. So, the role of actuators in the smart city was inclusively scrutinized employing bibliometric review. An organized practise was utilized to accumulate journal articles on actuators in smart cities. Screening processes have been devised for citation analyses. The utmost prolific authors, journals, articles, counties, and institutions are graded based on the overall citation count. Researching hubs and patterns were established utilizing author-keywords and index-keyword scrutiny on actuators in smart cities. The bibliometric assessment of the actuators in smart cities might deliver imperative evidence about the development of worldwide research. It is also very beneficial for upcoming researchers in the area of sensors and actuators in smart cities.

Features-Aware DDoS Detection in Heterogeneous Smart Environments based on Fog and Cloud Computing

Nowadays, urban environments are deploying smart environments (SEs) to evolve infrastructures, resources, and services. SEs are composed of a huge amount of heterogeneous devices, i.e., the SEs have both personal devices (smartphones, notebooks, tablets, etc) and Internet of Things (IoT) devices (sensors, actuators, and others). One of the existing problems of the SEs is the detection of Distributed Denial of Service (DDoS) attacks, due to the vulnerabilities of IoT devices. In this way, it is necessary to deploy solutions that can detect DDoS in SEs, dealing with issues like scalability, adaptability, and heterogeneity (distinct protocols, hardware capacity, and running applications). Within this context, this article presents an Intelligent System for DDoS detection in SEs, applying Machine Learning (ML), Fog, and Cloud computing approaches. Additionally, the article presents a study about the most important traffic features for detecting DDoS in SEs, as well as a traffic segmentation approach to improve the accuracy of the system. The experiments performed, using real network traffic, suggest that the proposed system reaches 99% of accuracy, while reduces the volume of data exchanged and the detection time.

Multi-Agent DRL for Task Offloading and Resource Allocation in Multi-UAV Enabled IoT Edge Network

The Internet of Things (IoT) edge network has connected lots of heterogeneous smart devices, thanks to unmanned aerial vehicles (UAVs) and their groundbreaking emerging applications. Limited computational capacity and energy availability have been major factors hindering the performance of edge user equipment (UE) and IoT devices in IoT edge networks. Besides, the edge base station (BS) with the computation server is allowed massive traffic and is vulnerable to disasters. The UAV is a promising technology that provides aerial base stations (ABSs) to assist the edge network in enhancing the ground network performance, extending network coverage, and offloading computationally intensive tasks from UEs or IoT devices. In this paper, we deploy a clustered multi-UAV to provide computing task offloading and resource allocation services to IoT devices. We propose a multi-agent deep reinforcement learning (MADRL)-based approach to minimize the overall network computation cost while ensuring the quality of service (QoS) requirements of IoT devices or UEs in the IoT network. We formulate our problem as a natural extension of the Markov decision process (MDP) concerning stochastic game, to minimize the long-term computation cost in terms of energy and delay. We consider the stochastic time-varying UAVs’ channel strength and dynamic resource requests to obtain optimal resource allocation policies and computation offloading in aerial to ground (A2G) network infrastructure. Simulation results show that our proposed MADRL method reduces the average costs by 38.643%, and 55.621% and increases the reward by 58.289% and 85.289% compared with the different single agent DRL and heuristic schemes, respectively.

Environment-driven Communication in Battery-free Smart Buildings

Recent years have witnessed the design and development of several smart devices that are wireless and battery-less. These devices exploit RFID backscattering-based computation and transmissions. Although singular devices can operate efficiently, their coexistence needs to be controlled, as they have widely varying communication requirements, depending on their interaction with the environment. The design of efficient communication protocols able to dynamically adapt to current device operation is quite a new problem that the existing work cannot solve well. In this article, we propose a new communication protocol, called ReLEDF, that dynamically discovers devices in smart buildings and their active and nonactive status and when active their current communication behavior (through a learning-based mechanism) and schedules transmission slots (through an Earliest Deadline First-- (EDF) based mechanism) adapt to different data transmission requirements. Combining learning and scheduling introduces a tag starvation problem, so we also propose a new mode-change scheduling approach. Extensive simulations clearly show the benefits of using ReLEDF, which successfully delivers over 95% of new data samples in a typical smart home scenario with up to 150 heterogeneous smart devices, outperforming related solutions. Real experiments are also conducted to demonstrate the applicability of ReLEDF and to validate the simulations.

TONTA: Trend-based Online Network Traffic Analysis in ad-hoc IoT networks

Internet of Things (IoT) refers to a system of interconnected heterogeneous smart devices communicating without human intervention. A significant portion of existing IoT networks is under the umbrella of ad-hoc and quasi ad-hoc networks. Ad-hoc based IoT networks suffer from the lack of resource-rich network infrastructures that are able to perform heavyweight network management tasks using, e.g. machine learning-based Network Traffic Monitoring and Analysis (NTMA) techniques. Designing light-weight NTMA techniques that do not need to be (re-) trained has received much attention due to the time complexity of the training phase. In this study, a novel pattern recognition method, called Trend-based Online Network Traffic Analysis (TONTA), is proposed for ad-hoc IoT networks to monitor network performance. The proposed method uses a statistical light-weight Trend Change Detection (TCD) method in an online manner. TONTA discovers predominant trends and recognizes abrupt or gradual time-series dataset changes to analyze the IoT network traffic. TONTA is then compared with RuLSIF as an offline benchmark TCD technique. The results show that TONTA detects approximately 60% less false positive alarms than RuLSIF.

Resource Efficient Deployment and Data Aggregation in Pervasive IoT Applications (Smart Agriculture)

Aims: Internet of Things (IoT) is the evolution of the Internet designed to sense, collect, analyze and distribute the data via IoT devices that form its core component. An important aspect of pervasive IoT applications is its resource-constrained devices. Most of the real-time Edge-IoT applications generate a huge amount of data, which add to the resource consumption in these devices. To save resources in such applications, efficient node deployment and data aggregation techniques can be used. This paper presents the design and modeling of node deployment and data aggregation in Edge-IoT applications along with the homogeneous and heterogeneous network scenarios for smart agriculture. Objectives: To save resources in such applications, efficient node deployment and data aggregation techniques can be used. This paper presents the design and modeling of node deployment and data aggregation in Edge-IoT applications along with the homogeneous and heterogeneous network scenarios for smart agriculture. Methods: For heterogeneous scenarios, we propose a clustering approach, Superior Aggregator Resource Efficient Clustering (SAREC), to address the resource constraints in pervasive Edge-IoT applications. The comparison of homogeneous and heterogeneous networks is based on LEACH and SAREC protocols, respectively. Results: The results show that SAREC is 25% more efficient in energy utilization and network lifetime than LEACH. The results also show that SAREC is more efficient in terms of storage and processing time as compared to LEACH. Conclusion: Node deployment is an important aspect in determining the architecture, which plays an important role in resource management in pervasive applications of IoT. The IoT nodes are distributed in a selected geographical location and the topology of the network is pre-decided to form an Edge-IoT network. In such an environment, the nodes are deployed to sense, aggregate and analyze the data. This paper presents a pervasive Edge-IoT network along with the mathematical modeling consisting of deployment and aggregation models. An Edge-IoT network for smart agriculture has been deployed and analysis of resource utilization has been performed in homogeneous and heterogeneous scenarios of the network. The resource limitations in pervasive IoT network motivated us to develop a SAREC approach for such Edge-IoT applications that optimizes the use of resources. The comparison of the proposed SAREC protocol is made with respect to LEACH protocol on the basis of energy, network lifetime, number of alive nodes, storage and processing time. The results show that SAREC protocol is 25% more efficient in energy utilization and network lifetime than LEACH. It is also evident from the results that the SAREC is more efficient in terms of storage and processing time as compared to LEACH.

A distributable event-oriented architecture for activity recognition in smart homes

In this paper, a new architecture is proposed for continuously generating, propagating, and delivering information by using event-based communication between independent agents. The resulting system can both handle heterogeneous smart environments and compute information in multiple places. With a communication method working as an abstraction layer, the proposed solution enables the use of multiple technologies at once. Additionally, different options for delivering the resulting data to client applications are explored. The implementation of this design as a platform written in Java with the Spring Framework is also presented, along with its handling of ten housing facilities equipped with various sensors (electromagnetic contacts, smart plugs, motion detectors, humidity, temperature, and light sensors). This paper is then concluded by an analysis of the platform workloads incurred by the tracking of a set of low-level activities. Finally, the code is distributed online for the benefit of the community.

On the optimality of Concurrent Container Clusters Scheduling over heterogeneous smart environments

Smart environments rely on the cloud for most computation activities; however, leveraging the availability of resources at the edge could complement the cloud capabilities to provide a resources continuum spectrum that takes advantage of the benefits of both technologies, cloud and edge computing. In this scenario, applications are usually decomposed for execution in sets of tasks, which are in turn encapsulated into virtual components such as containers. Containers are lightweight implementations of virtual machines, improving efficiency and portability in distributed applications. Traditional containers scheduling in cloud is a well-known problem, but when the environment is heterogeneous, as it is in the scope of edge computing and edge-cloud systems, the problem becomes more challenging. In this paper we present the Concurrent Container Clusters Scheduling problem (C3S) aimed at optimizing the problem of placing containers in clusters of heterogeneous nodes satisfying a set of resource requirements, quality of service limitations, and considering additional stringent constraints in terms of applications execution in isolation for security guaranteeing. The C3S problem has been formulated using Integer Linear Programming with the dual objective of minimizing the number of applications rejected while minimizing the number of nodes used for computation. We have evaluated the optimality of this approach, analyzed the performance in terms of solving time and, finally, created a heuristic approach to solve the problem in realistic high demanding scenarios.

Towards Interoperable Blockchains: A Survey on the Role of Smart Contracts in Blockchain Interoperability

The slower than expected adoption rate of blockchain technology has highlighted that there are barriers due to the diversity of its applications and its users. To overcome this limitation and take full advantage of the novel technology, researchers from academia as well as industry are dedicated to find different solutions, where two or more blockchains can interact with each other. As a result, several interoperability solutions have presented themselves. To investigate the functionalities and underlying mechanisms of interoperable blockchain solutions, researchers have conducted several surveys by discussing the features and innovations of these methods. However, the existing surveys tend to focus on the architectural description of the interoperability solutions and completely overlook the most promising aspect of blockchain adaptability, namely the smart contract. This paper fills the gap by exploring the role of smart contracts in blockchain interoperability solutions. Our research has classified the existing interoperability solutions into three main categories: heterogeneous blockchains and homogeneous smart contracts, homogeneous blockchains and homogeneous smart contracts, heterogeneous blockchains, and heterogeneous smart contracts. To provide a systematic overview of the smart contracts used in blockchain interoperability, each category is further divided into subcategories by identifying the functionalities of the smart contract used. Based on our survey, a taxonomy is proposed to help classify the blockchain interoperability solutions. The interoperability solutions in each category are analyzed in-depth, and the results are presented in tabular format to illustrate the characteristics of the interoperability solutions in a meaningful way. Finally, a number of open issues and research directions are discussed to overcome the limitations and improve the performance of blockchain interoperability.

ICS: Interoperable Communication System for Inter-Domain Routing in Internet-of-Things

The Internet-of-Things consists of heterogeneous smart appliances connected by global network with self-configuring capabilities requiring interoperable communication schemes while performing inter-domain routing. A review of existing interoperable approaches shows that there is still a large scope of improving IoT interoperability. The proposed system introduces Interoperable Communication System (ICS) by developing a novel inter-domain routing in IoT using two schemes. Preemptive and Non-Preemptive Communication scheme targets mainly emergency-based routing, which demands faster transmission, and dedicated transmission, demanding accountability in communication. A simulation study carried out for the proposed system shows that it offers approximately 90% reduced delay, 57% increased packet delivery ratio, and 98% faster processing time when compared with existing approaches to accomplish interoperability in IoT.

Making heterogeneous smart home data interoperable with the SAREF ontology

Making heterogeneous smart home data interoperable with the SAREF ontology

Making heterogeneous smart home data interoperable with the SAREF ontology

Making heterogeneous smart home data interoperable with the SAREF ontology

Towards Machine Learning Based Intrusion Detection in IoT Networks

The Internet of Things (IoT) integrates billions of self-organized and heterogeneous smart nodes that communicate with each other without human intervention. In recent years, IoT based systems have been used in improving the experience in many applications including healthcare, agriculture, supply chain, education, transportation and traffic monitoring, utility services etc. However, node heterogeneity raised security concern which is one of the most complicated issues on the IoT. Implementing security measures, including encryption, access control, and authentication for the IoT devices are ineffective in achieving security. In this paper, we identified various types of IoT threats and shallow (such as decision tree (DT), random forest (RF), support vector machine (SVM)) as well as deep machine learning (deep neural network (DNN), deep belief network (DBN), long short-term memory (LSTM), stacked LSTM, bidirectional LSTM (Bi-LSTM)) based intrusion detection systems (IDS) in the IoT environment have been discussed. The performance of these models has been evaluated using five benchmark datasets such as NSL-KDD, IoTDevNet, DS2OS, IoTID20, and IoT Botnet dataset. The various performance metrics such as Accuracy, Precision, Recall, F1-score were used to evaluate the performance of shallow/deep machine learning based IDS. It has been found that deep machine learning IDS outperforms shallow machine learning in detecting IoT attacks.

Blockchain and Federated Learning for 5G Beyond

In 5G and beyond networks, the increasing inclusion of heterogeneous smart devices and the rising privacy and security concerns, are two crucial challenges in terms of computation complexity and privacy preservation for Artificial Intelligence (AI)-based solutions. In this regard, federated learning emerges as a new technique, which enlarges the scale of training data, and protects the privacy of user data. The development of edge computing makes it possible to apply federated learning to beyond 5G. However, the security of local parameters, the learning quality, and the varying computing and communication resources, are crucial issues that remain unexplored in federated learning schemes. In this article, we propose a block-chain empowered federated learning framework, and present its potential application scenarios in beyond 5G. We enhance the security and privacy by integrating blockchain into a federated learning scheme for maintaining the trained parameters. In particular, we formulate the resource sharing task as a combinational optimization problem while taking resource consumption and learning quality into account. We design a deep reinforcement learning based algorithm to find an optimal solution to the problem. Numerical results show that the proposed scheme achieves high accuracy and good convergence.

Performance Analysis and Enhancement of Free Space Optical Links for Developing State-of-the-Art Smart City Framework

In this paper, we have investigated and reported the performance of free-space optical (FSO) links operating in adverse atmospheric conditions. Since FSO links share operational similarity with fiber communication; hence, we believe that a cost-effective FSO framework can play a significant role in the transparent integration of high-speed network access backbones with the end-users. Different modulation formats, complemented with spatial diversity techniques, are discussed in this paper to strategize performance optimization of FSO links. Using bit error rate (BER) and signal-to-noise ratio (SNR) as performance metrics, it was found that binary phase-shift keying (BPSK) qualifies as the best technique modulation technique delivering SNR gain of 10 dB over on–off keying (OOK) operating link under similar channel conditions. Further performance optimization was achieved using space diversity reception wherein SNR witnessed a gain of 3 dB gain over the single-channel FSO link. In terms of application, the proposed model can help in developing a citizen-centric smart city ecosystem that can support seamless communication between heterogeneous smart devices.

A Query based Information search in an Individual’s Small World of Social Internet of Things

Recently, the consolidation of Social Network and the Internet of Things (IoT) acquiring appreciation and consideration among researchers due to its immense and adaptable behavior. Social Internet of Things (SIoT) comprehends the adequacy to support the inventive applications and network services with adept and persuasive demeanors. The author’s exertion for this research provides the subsistence to the SIoT amelioration Currently, the SIoT technique proven to be competent, but the exponential growth of heterogeneous smart devices may arise a challenging scenario. Searching the right object, service, or best path to the informative nodes in such highly convoluted networks is always a challenging task. This research strengthen the SIoT paradigm by consolidating the properties of the individual’s Small World. In addition, this research also advises a Smart Social Agent (SSA) to minimize the human intervention. The absolute objective of this paper is two-fold, (i) transformation of SIoT network into Individual’s Small World SIoT network, (ii) searching the right information efficiently by exploiting SSA. To accomplish the goal we have proposed an efficient Query based Searching algorithm which exploits Service Oriented properties of Individual’s Small World SIoT network and searches information. First result shows the significant alleviation in the network complexity, reduces the average path length, and network diameter. Eventually, it improves the network scalability and navigability. While second result shows that the proposed Query based searching algorithm proves to be efficient with its mean execution time not more than 2.66 ms for complete network on two different machines. Whereas, for Individual’s small world network the mean execution time is not more than 2.47 ms for both machine.

Secured smart mobile app for smart home environment

Smart home appliances like smart door, smart AC, smart lights etc., are the facilities which can be utilized because of advancements in Internet of Things. However, security of these smart home appliances is very much important to avoid security threats. Existing smart home appliances are facing problems with security issues such as such as hacking, mishandling of devices etc., due to one stage authentication process. The proposed Secured Smart Mobile App for Smart Home Environment (SSMASHE) provides a two stage authentication process which can secure smart home appliances. It consists of three modules, first one is smart things, next one smart mobile app and the third module is big data platform. In this paper a smart home environment embedding with smart mobile app has been simulated to evaluate the security mechanism through authentication process. In this process the delay has been calculated for the purpose of fast communication and security assigned to big data environment. The proposed method delay time is less when compared to Fast Identity Online (FIDO) method. The proposed method is comfortable to real time heterogeneous smart home IoT devices because this process follows the asynchronous authentication procedure.

FEM流体構造連成解析による 新型形状津波消波堤の最適配置と洋上風力発電を組合せた Heterogeneous Smart Technology Unit (HSTU)化の提案

FEM流体構造連成解析による 新型形状津波消波堤の最適配置と洋上風力発電を組合せた Heterogeneous Smart Technology Unit (HSTU)化の提案