Things Edge Research Articles

Class imbalance and concept drift invariant online botnet threat detection framework for heterogeneous IoT edge

Heterogeneous networks (HetIoT) of high-capacity and resource-constrained IoT devices and their edge associations for on-device distributed critical workloads—called the edge-of-things (EoT)—attract short-burst, botnet-based zero-day attacks that exploit latent vulnerabilities due to heterogeneous device properties, dynamic operational contexts, and insufficient security scrutiny of the constituent proprietary devices. Such a scenario necessitates a device-specific network intrusion detection (NID) technique for localizing the threat space and updated rule learning through online (real-time) model retraining. Furthermore, scarce labeled knowledge base and high levels of class imbalance of NID datasets complicate the ID system design process for EoT environments, as online detection cannot afford computationally expensive data balancing techniques; this necessitates a class imbalance invariant traffic inference technique for data preprocessing. Therefore, we propound the ONIDS online NID technique, which consists of a two-fold solution for the above problems. First, we propose a Beta distribution-based inference technique for efficient traffic behavior approximation—invariant of class imbalance and capable of non-cumulative traffic processing of smaller sample sizes. Then, we put forth an online ID technique called ELMO for class imbalance invariant time-bound training of smaller sample sizes on resource-constrained device-specific network traffic. Together, they are invariant of traffic class imbalances and adaptable to resultant concept drift categories exhibited by HetIoT attack behaviors. ONIDS has low memory and compute footprints and can efficiently process large and small amounts of traffic, making it suitable for online and offline NID. It also exhibits qualitative and quantitative superiority—particularly on smaller data samples.

Vehicular Visible Light Communication for Intersection Management

An innovative treatment for congested urban road networks is the split intersection. Here, a congested two-way–two-way traffic light-controlled intersection is transformed into two lighter intersections. By reducing conflict points and improving travel time, it facilitates smoother flow with less driver delay. We propose a visible light communication system based on Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I) and Infrastructure-to-Vehicle (I2V) communications able to safely manage vehicles crossing through an intersection, leveraging Edge of Things (EoT) facilities. Headlights, street lamps, and traffic signals are used by connected vehicles to communicate with one another and with infrastructure. Through internally installed Driver Agents, an Intersection Manager coordinates traffic flow and interacts with vehicles. For the safe passage of vehicles across intersections, request/response mechanisms and time and space relative pose concepts are used. A virtual scenario is proposed, and a “mesh/cellular” hybrid architecture used. Light signals are emitted by transmitters by encoding, modulating, and converting data. Optical sensors with light-filtering properties are used as receivers and decoders. The VLC request/response concept uplink and downlink communication between the infrastructure and the vehicles is tested. Based on the results, the short-range mesh network provides a secure communication path between street lamp controllers and edge computers through neighbor traffic light controllers that have active cellular connections, as well as peer-to-peer communication, allowing V-VLC ready cars to exchange information.

Intelligent AI-based Healthcare Cyber Security System using Multi-Source Transfer Learning Method

Cyber-security intelligence have made a great impact over healthcare industry where several researchers are developing new techniques to improve security for healthcare systems. Besides, Artificial Intelligence (AI) become the tremendous technology in recent decades to improve the existing methods to be more intelligent. In this paper, we proposed cyber attack detection system for healthcare sector with centralized and federated transfer learning mode. Edge of Things (EoT) framework is developed in connection with cloud and healthcare sectors to transmit the data efficiently and the proposed Centralized with Multi-Source Transfer Learning (CMTL) algorithm which is used for detection and classification of various threats such as information gathering, DoS/DDoS attacks, Malware attacks, Injection attacks, and Man in the Middle attacks. Performance of the proposed framework is evaluated using various datasets such as EMNIST, X-IIoTID, and Federated TON_IoT. Our framework outperforms with the analysis of execution time and obtains high level accuracy when compared with different algorithms.

Toward Performance and Energy-Efficient Edge-of-Things

Edge computing is a recent trend wherein a (large) number of Internet of Things (IoT) devices are being connected to the cloud via edge devices. Although edge devices generally have (slightly) more computing power than IoT devices, several strategies can help to improve performance and reduce energy consumption. The key research challenge here is how to ensure efficient resource utilization, particularly for real-time delay-sensitive applications, such as military and battlefield contexts, and real-time disaster recovery and response efforts. Focusing on edge computing with a (large) number of IoT devices, we propose a dynamic policy-based computing approach to improve the overall performance of Edge-of-Things (EoT). We evaluated the energy consumption of heterogeneous edge-only resources for given policies to demonstrate their impact on an EoT computing infrastructure. Finally, we discussed open issues and future research directions in green EoT computing.

Edge computing based secure health monitoring framework for electronic healthcare system.

Nowadays, Smart Healthcare Systems (SHS) are frequently used by people for personal healthcare observations using various smart devices. The SHS uses IoT technology and cloud infrastructure for data capturing, transmitting it through smart devices, data storage, processing, and healthcare advice. Processing such a huge amount of data from numerous IoT devices in a short time is quite challenging. Thus, technological frameworks such as edge computing or fog computing can be used as a middle layer between cloud and user in SHS. It reduces the response time for data processing at the lower level (edge level). But, Edge of Things (EoT) also suffers from security and privacy issues. A robust healthcare monitoring framework with secure data storage and access is needed. It will provide a quick response in case of the production of abnormal data and store/access the sensitive data securely. This paper proposed a Secure Framework based on the Edge of Things (SEoT) for Smart healthcare systems. This framework is mainly designed for real-time health monitoring, maintaining the security and confidentiality of the healthcare data in a controlled manner. This paper included clustering approaches for analyzing bio-signal data for abnormality detection and Attribute-Based Encryption (ABE) for bio-signal data security and secure access. The experimental results of the proposed framework show improved performance with maintaining the accuracy of up to 98.5% and data security.

Blockchain for Edge of Things: Applications, Opportunities, and Challenges

In recent years, blockchain networks have attracted significant attention in many research areas beyond cryptocurrency, one of them being the Edge of Things (EoT) that is enabled by the combination of edge computing and the Internet of Things (IoT). In this context, blockchain networks enabled with unique features, such as decentralization, immutability, and traceability, have the potential to reshape and transform the conventional EoT systems with higher security levels. Particularly, the convergence of blockchain and EoT leads to a new paradigm, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BEoT</i> that has been regarded as a promising enabler for future services and applications. In this article, we present a state-of-the-art review of recent developments in the BEoT technology and discover its great opportunities in many application domains. We start our survey by providing an updated introduction to blockchain and EoT along with their recent advances. Subsequently, we discuss the use of BEoT in a wide range of industrial applications, from smart transportation, smart city, smart healthcare to smart home, and smart grid. Security challenges in the BEoT paradigm are also discussed and analyzed, with some key services, such as access authentication, data privacy preservation, attack detection, and trust management. Finally, some key research challenges and future directions are also highlighted to instigate further research in this promising area.

Toward Blockchain for Edge-of-Things: A New Paradigm, Opportunities, and Future Directions

Blockchain is gaining momentum as a promising technology for many application domains, one of them being the Edge-of-Things (EoT) that is enabled by the integration of edge computing and the Internet-of-Things (IoT). Particularly, the amalgamation of blockchain and EoT leads to a new paradigm, called blockchain enabled EoT (BEoT) that is crucial for enabling future low-latency and high-security services and applications. This article envisions a novel BEoT architecture for supporting industrial applications under the management of blockchain at the network edge in a wide range of IoT use cases such as smart home, smart healthcare, smart grid, and smart transportation. The potentials of BEoT in providing security services are also explored, including access authentication, data privacy preservation, attack detection, and trust management. Finally, we point out some key research challenges and future directions in this emerging area.

Service Versus Protection: A Bayesian Learning Approach for Trust Provisioning in Edge of Things Environment

Edge of Things (EoT) technology enables end-users participation with smart sensors and mobile devices (such as smartphones and wearable devices) to the smart devices across the smart city. Trust management is the main challenge in EoT infrastructure to consider the trusted participants. The Quality of Service (QoS) is highly affected by malicious users with fake or altered data. In this article, a robust trust management (RTM) scheme is designed based on Bayesian learning and collaboration filtering. The proposed RTM model is regularly updated after a specific interval with the significant decay value to the current calculated scores to update the behavior changes quickly. The dynamic characteristics of edge nodes are analyzed with the new probability score mechanism from recent services’ behavior. The performance of the proposed trust management scheme is evaluated in a simulated environment. The percentage of collaboration devices is tuned as 10%, 50%, and 100%. The maximum accuracy of 99.8% is achieved from the proposed RTM scheme. The experimental results demonstrate that the RTM scheme shows better performance than the existing techniques in filtering malicious behavior and accuracy.

DaaS: Dew Computing as a Service for Intelligent Intrusion Detection in Edge-of-Things Ecosystem

Edge of Things (EoT) enables the seamless transfer of services, storage, and data processing from the cloud layer to edge devices in a large-scale distributed Internet of Things (IoT) ecosystems (e.g., Industrial systems). This transition raises the privacy and security concerns in the EoT paradigm distributed at different layers. Intrusion detection systems (IDSs) are implemented in EoT ecosystems to protect the underlying resources from attackers. However, the current IDSs are not intelligent enough to control the false alarms, which significantly lower the reliability and add to the analysis burden on the IDSs. In this article, we present a Dew Computing as a Service (DaaS) for intelligent intrusion detection in EoT ecosystems. In DaaS, a deep learning-based classifier is used to design an intelligent alarm filtration mechanism. In this mechanism, the filtration accuracy is improved (or sustained) by using deep belief networks. In the past, the cloud-based techniques have been applied for offloading the EoT tasks, which increases the middle layer burden and raises the communication delay. Here, we introduce the dew computing features that are used to design the smart false alarm reduction system. DaaS, when experimented in a simulated environment, reflects lower response time to process the data in the EoT ecosystem. The revamped DBN model achieved the classification accuracy up to 95%. Moreover, it depicts a 60% improvement in the latency and 35% workload reduction of the cloud servers as compared to edge IDS.

A multi-agent autonomous intersection management (MA-AIM) system for smart cities leveraging edge-of-things and Blockchain

The growing trend of people moving around urban areas requires more intelligent transportation-support services in smart cities. In this context, the number of traffic collisions, especially in intersections, has rapidly increased as well as the number of vehicles. The leading cause of this is represented by human errors in recognition and decision making. Thus, the development of secure Autonomous Intersection Management (AIM) systems is an emerging challenge. In this paper, we propose a Multi-Agent AIM (MA-AIM) system based on Vehicle-to-Infrastructure (V2I) and Infrastructure-to-Vehicle (I2V) communications able to safely manage vehicles crossing through an intersection leveraging both Edge of Things (EoT) and Blockchain facilities. The proposed system includes an Intersection Manager Agent (IMA) that: i) interacts with vehicles through Driver Agents (DAs) installed on them in an EoT environment, and ii) uses Blockchain mechanisms in order to orchestrate the passage of vehicles crossing the intersection safely. In particular, we implemented a proof-of-concept integrating the AIM 4.0 simulator with Hyperledger Fabric (implementing Blockchain capabilities) and with Node-RED (implementing V2I/I2V communications in an EoT environment). Preliminary experiments show the feasibility and potentiality of our solution.

Intrusion detection in Edge-of-Things computing

Edge-of-Things (EoT) is a new evolving computing model driven by the Internet of Things (IoT). It enables data processing, storage, and service to be shifted from the Cloud to nearby Edge devices/systems such as smartphones, routers, and base stations on the IoT paradigm. However, this architectural shift causes the security and privacy issues to migrate to the different layers of the Edge architecture. Therefore, detecting intrusion in such a distributed environment is difficult. In this scenario, an Intrusion Detection Systems is necessary. Here, we propose an approach to quickly and accurately detect intrusive activities in the EoT network, to realize the full potential of the IoT. Specifically, we propose a deep belief network (DBN) based on an advanced intrusion detection approach. We studied different detection models, by using different structures of DBNs, and compared them with existing detection techniques. Test results show that the proposed methodology performs essentially superior to the current state-of-the-art approaches.

Mildip: An energy efficient code offloading framework in mobile cloudlets

The explosive growth of connected mobile devices in Edge of Things (EoT) computing offers an opportunity to support high-quality life in smart cities but also results in an extremely high energy consumption in battery limited mobile devices. Accordingly, to increase the battery life or improve performance of mobile devices, code offloading has been proposed. Moreover, with the development of the technology about low-latency and low-power of device-to-device communication, offloading codes to surrounding mobile devices in nearby cloudlets has been widely applied. However, the existing works on the mobile cloudlets mostly focus on code distribution and scheduling but ignores the affection of the energy efficiency of CPU. According to the CPU Energy/Frequency Convexity Rule, there is an optimal clock frequency that can minimize energy consumption. In recent years, a novel processor architectures with adjustable frequency have been proposed to deliver various kinds of energy efficiency and CPU performance. Taking the processor frequency into consideration, we propose an energy-efficient code offloading framework as MilDip to offload partitioned heavy tasks to around mobile devices and to operate these tasks in the state of low CPU performance and high energy efficiency. We formulate offloading problem as a mixed-integer nonlinear optimization problem, with the goal of energy efficiency maximization. Based on the formulation, we further propose a heuristic algorithm COFS based on CPU frequency scaling. Extensive simulated experiments are provided to evaluate the performance of our method. The simulation results show that up to 77% of energy can be saved by using COFS compared to the local execution. Moreover, when enough devices surround the terminal, COFS energy consumption tends to stabilize. From the experiments, we can also conclude that COFS can save more about 20% ~ 50% energy consumption comparing to other schemes.

Energy efficient edge-of-things

Edge-of-Things (EoT) emerged as a novel computing and storage paradigm to overcome the limitations of IoT-cloud environment by providing cloud-like services at edge of the network. EoT offers a vast area for research and development as the invention has laid out great opportunities to experiment the possibilities for handling large data sets produced by the growing Internet-of-Things (IoT). The EoT offers a framework that lies between the cloud-to-end to perform the processing and cater the storage demands of the IoT applications. However, the exponential increase in EoT infrastructure resulted into extreme energy consumption. This paper finds the opportunity to address the issue of energy consumption in IoT-EoT environment by introducing dynamic speed scaling mechanism in EoT devices. The proposed approach is rigorously evaluated, and the verification is acquired through the simulations carried out on the simulator, iFogSim. The results show significant improvement in energy conservation by dynamically scaling the processor frequency of EoT devices according to the load variations in IoT traffic.

Secure Edge of Things for Smart Healthcare Surveillance Framework

The vast development of the Internet of Things (IoT) and cloud-enabled data processing solutions provide the opportunity to build novel and fascinating smart, connected healthcare systems. Smart healthcare systems analyze the IoT-generated patient data to both enhance the quality of patient care and reduce healthcare costs. A major challenge for these systems is how the Cloud of Things can handle the data generated from billions of connected IoT devices. Edge computing infrastructure offers a promising solution by operating as a middle layer between the IoT devices and cloud computing. The Edge of Things (EoT) can offer small-scale real-time computing and storage capabilities that ensures low latency and optimal utilization of the IoT resources. However, the EoT has privacy-preservation issues, which is a significant concern for the healthcare systems that contain sensitive patient data. This paper introduces a novel EoT computing framework for secure and smart healthcare surveillance services. Fully homomorphic encryption preserves data privacy and is stored and processed within an EoT framework. A distributed approach for clustering-based techniques is developed for the proposed EoT framework with the scalability to aggregate and analyze the large-scale and heterogeneous data in the distributed EoT devices independently before it is sent to the cloud. We demonstrate the proposed framework by evaluating a case study for the patient biosignal data. Our framework rapidly accelerates the analysis response time and performance of the encrypted data processing while preserving a high level of analysis accuracy and data privacy.

Enabling cognitive contributory societies using SIoT:: QoS aware real-time virtual object management

Internet of things (IoT) has initiated a few interesting research directions such as Social Internet of Things(SIoT), Cloud of Things(CoT), and Edge of Things(EoT) etc. Nowadays, a large number of IoT nodes are available in our surroundings and there is a need to ensure automated access and communication among these IoT nodes. An emerging area of study is to make a social platform for IoT nodes so that devices can communicate with each other and create automated and on-demand services. As these IoT nodes and services are going to co-exist with us (human), we foresee establishing of cognitive contributory skills where IoT nodes, services and even human skills can collectively form a cognitive society to share resources, information and skills.However, building such intelligent societies automatically using SIoT is a big challenge, mainly due to the complexity of the systems and availability of a large number of nodes. In such scenarios, it is not trivial to find a suitable SIoT service node correctly to avail a service in real-time. We propose a common platform to virtualize the physical objects and make them available in the cyber-world and at the same time to ensure resource sharing. In this paper, we focus on virtual object management and selection process for SIoT platform. We propose a QoS aware object selection using Integer Programming solutions to find a right service at the right time.

Edge-of-things computing framework for cost-effective provisioning of healthcare data

Edge-of-Things (EoT)-based healthcare services are forthcoming patient-care amenities related to autonomic and persuasive healthcare, where an EoT broker usually works as a middleman between the Healthcare Service Consumers (HSC) and Computing Service Providers (CSP). The computing service providers are the edge computing service providers (ECSP) and cloud computing service provider (CCSP). Sensor observations from a patient’s body area networks (BAN) and patients’ medical and genetic historical data are very sensitive and have a high degree of interdependency. It follows that EoT based patient monitoring systems or applications are tightly coupled and require obstinate synchronization. Therefore, this paper proposes a portfolio optimization solution for the selection of virtual machines (VMs) of edge and/or cloud computing service providers. The dynamic pricing for an EoT computation service is considered by the EoT broker for optimal VM provisioning in an EoT environment. The proposed portfolio optimization solution is compared with the traditional certainty equivalent approach. As the portfolio optimization is a centralized solution approach, this paper also proposes an alternating direction method of multipliers (ADMM) based distributed provisioning method for the healthcare data in the EoT computing environment. A comparative study shows the cost-effective provisioning for the healthcare data through portfolio optimization and ADMM methods over the traditional certainty equivalent and greedy approach, respectively.