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The Internet of Behavior is the recent trend in the Internet of Things (IoT), which analyzes the behaviour of individuals using huge amounts of data collected from their activities. The behavioural data collection process from an individual to a data center in the network layer of the IoT is addressed by the Routing Protocol for Low-powered Lossy Networks (RPL) downward routing policy. A hybrid mode of operation in RPL is designed to minimize the limitations of standard modes of operations in the downward routing of RPL. The existing hybrid modes use the common parameters, such as routing table capacity, energy level, and hop-count for making storing mode decisions at each node. However, none of these works have utilized the deciding parameters, such as number of Destination-Oriented Directed Acyclic Graph (DODAG) children, rank, and transmission traffic density for this purpose. In this article, we propose two hybrid MOPs for RPL focusing on the aspect of efficient downward communication for the Internet of Behaviors. The first version decides the mode of each node based on the rank and number of DODAG children of the node. In addition, the proposed Mode of Operation (MOP) has the provision to balance the task of a storing node that is currently running on low power and computational resources by a handover mechanism among the ancestors. The second version of the hybrid MOP utilizes the upward and downward transmission traffic probabilities together with 170 rule or 1D cellular automata to decide the operating mode of a node. The analysis on the upper bound on communication shows that both proposed works have communication overhead nearly equal to the storing mode. The experimental results also infer that the proposed adaptive MOP have lower communication overhead compared with standard storing modes and existing schemes ARPL, MERPL, and HIMOPD. [ABSTRACT FROM AUTHOR]

Collaborative Edge Computing (CEC) is a distributed approach to data processing in the Internet of Things (IoT) that reduces latency and enhances scalability. CEC allows data processing to occur closer to its source, at the "edge" of the network, which is particularly beneficial for real-time applications. However, security and data privacy are major concerns in CEC due to the vulnerability of edge devices and the sensitive nature of the data collected. This special issue includes four papers that propose solutions to these challenges, such as a scheduling framework for efficient resource allocation, a hierarchical structured cluster routing protocol for load balancing, defense mechanisms against poisoning attacks in federated learning systems, and a decentralized identifier-based tracking system for digital content distribution. Overall, CEC has the potential to revolutionize the IoT landscape by enabling secure and scalable data processing. [Extracted from the article]

Analyzing packet loss, whether resulting from communication challenges or malicious attacks, is vital for broadcast authentication protocols. It ensures legitimate and continuous authentication across networks. While previous studies have mainly focused on countering Denial of Service (DoS) attacks' impact on packet loss, our research introduces an innovative investigation into packet loss and develops data recovery within variant TESLA protocols. We highlight the efficacy of our proposed hybrid TLI-µTESLA protocol in maintaining continuous and robust connections among network members, while maximizing data recovery in adverse communication conditions. The study examines the unique packet structures associated with each TESLA protocol variant, emphasizing the implications of losing each type on the network performance. We also introduce modifications to variant TESLA protocols to improve data recovery and alleviate the effects of packet loss. Using Java programming language, we conducted simulation analyses that illustrate the adaptability of variant TESLA protocols in recovering lost packet keys and authenticating previously buffered packets, all while maintaining continuous and robust authentication between network members. Our findings also underscore the superiority of the hybrid TLI-µTESLA protocol in terms of packet loss performance and data recovery, alongside its robust cybersecurity features, including confidentiality, integrity, availability, and accessibility. Additionally, we demonstrated the efficiency of our proposed protocol in terms of low computational and communication requirements compared to earlier TESLA protocol variants, as outlined in previous publications. [ABSTRACT FROM AUTHOR]

RPL (Routing Protocol for Low-Power and Lossy Networks) is a crucial and widely accepted routing protocol of the Internet of Things (IoT). RPL constructs similar to a tree structure for the data routing. For efficient routing, RPL offers a different mode of operations for effectively satisfying the different applications. We are considering several approaches and parameters, including other factors in this paper that contribute to designing the hybrid mode of operations. This paper provides a comprehensive and systematic survey of various hybrid modes of operations for RPL. We outline the challenges, methodologies in the pseudocode format, taxonomy and subsequently analyze all the possible format properties with different network conditions. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) has the potential to leverage the immense amount of operational data of clouds, services, and social and communication networks. As a concrete example, AI techniques have been adopted by telcom operators to develop virtual assistants based on advances in natural language processing (NLP) for interaction with customers and machine learning (ML) to enhance the customer experience by improving customer flow. Machine learning has also been applied to finding fraud patterns which enables operators to focus on dealing with the activity as opposed to the previous focus on detecting fraud. [ABSTRACT FROM AUTHOR]

Machine Learning (ML) and Internet of Things (IoT) are complementary advances: ML techniques unlock the potential of IoT with intelligence, and IoT applications increasingly feed data collected by sensors into ML models, thereby employing results to improve their business processes and services. Hence, orchestrating ML pipelines that encompass model training and implication involved in the holistic development lifecycle of an IoT application often leads to complex system integration. This article provides a comprehensive and systematic survey of the development lifecycle of ML-based IoT applications. We outline the core roadmap and taxonomy and subsequently assess and compare existing standard techniques used at individual stages. [ABSTRACT FROM AUTHOR]

The modern era of information and technology leverages connectivity and information sharing for enhancing applications and reducing human intervention. The proliferation of Internet of Things (IoT) in almost all aspects of modern life raises major challenges of security and privacy in health care services and the pharmaceutical industry. Blockchain has emerged as a secure technology in a trustless network without central governance. The inherent attributes it provides can solve the security and privacy challenges related to IoT and extend its capabilities. However, traditional instances of Blockchain are not suited for the IoT environment for reasons such as computationally expensive consensus process. Proof of authentication is a lightweight consensus algorithm that can be implemented in the IoT environment. This paper presents an IoT‐friendly Blockchain scheme implemented to evaluate the feasibility of medical supply and drug transportation to aid security and mitigate privacy issues which are fault‐tolerant, transparent, and traceable. The proof of concept implementation on portable single board computer demonstrates proof of authentication consensus algorithm executed with block validation and block addition in 30 and 40 ms while consuming 45 and 60mJ, respectively. Additionally, the network performance of the proposed architecture was presented and evaluated in the application context. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) is playing a promising role in e-healthcare applications in the recent decades; nevertheless, security is one of the crucial challenges in the current field of study. Many healthcare devices (for instance, a sensor-augmented insulin pump and heart-rate sensor) collect a user's real-time data (such as glucose level and heart rate) and send them to the cloud for proper analysis and diagnosis of the user. However, the real-time user's data are vulnerable to various authentication attacks while sending through an insecure channel. Besides that, the attacksmay further open scope formany other subsequent attacks. Existing security mechanisms concentrate on two-party mutual authentication. However, an IoT-enabled healthcare application involves multiple parties such as a patient, e-healthcare test-equipment, doctors, and cloud servers that requires multi-party authentication for secure communication. Moreover, the design and implementation of a lightweight security mechanism that fits into the resource constraint IoT-enabled healthcare devices are challenging. Therefore, this article proposes a lightweight, multi-party authentication and key-establishment protocol in IoT-based e-healthcare service access network to counter the attacks in resource constraint devices. The proposed multi-party protocol has used a lattice-based cryptographic construct such as Identity-Based Encryption (IBE) to acquire security, privacy, and efficiency. The study provided all-round analysis of the scheme, such as security, power consumption, and practical usage, in the following ways. The proposed scheme is tested by a formal security tool, Scyther, to testify the security properties of the protocol. In addition, security analysis for various attacks and comparison with other existing works are provided to show the robust security characteristics. Further, an experimental evaluation of the proposed scheme using IBE cryptographic construct is provided to validate the practical usage. The power consumption of the scheme is also computed and compared with existing works to evaluate its efficiency. [ABSTRACT FROM AUTHOR]

The Internet of Things is visualized as a fundamental networking model that bridges the gap between the cyber and real-world entity. Uniting the real-world object with virtualization technology is opening further opportunities for innovation in nearly every individual's life. Moreover, the usage of smart heterogeneous multimedia devices is growing extensively. These multimedia devices that communicate among each other through the Internet form a unique paradigm called the Internet of Multimedia Things (IoMT). As the volume of the collected data in multimedia application increases, the security, reliability of communications, and overall quality of service need to be maintained. Primarily, distributed denial of service attacks unveil the pervasiveness of vulnerabilities in IoMT systems. However, the Software Defined Network (SDN) is a new network architecture that has the central visibility of the entire network, which helps to detect any attack effectively. In this regard, the combination of SDN and IoMT, termed SD-IoMT, has the immense ability to improve the network management and security capabilities of the IoT system. This article proposes an SDNassisted two-phase detection framework, namely SD-IoMT-Protector, in which the first phase utilizes the entropy technique as the detection metric to verify and alert about the malicious traffic. The second phase has trained with an optimized machine learning technique for classifying different attacks. The outcomes of the experimental results signify the usefulness and effectiveness of the proposed framework for addressing distributed denial of service issues of the SD-IoMT system. [ABSTRACT FROM AUTHOR]

Summary: With the proliferation of Internet of Things (IoT) and edge computing paradigms, billions of IoT devices are being networked to support data‐driven and real‐time decision making across numerous application domains, including smart homes, smart transport, and smart buildings. These ubiquitously distributed IoT devices send the raw data to their respective edge device (eg, IoT gateways) or the cloud directly. The wide spectrum of possible application use cases make the design and networking of IoT and edge computing layers a very tedious process due to the: (i) complexity and heterogeneity of end‐point networks (eg, Wi‐Fi, 4G, and Bluetooth); (ii) heterogeneity of edge and IoT hardware resources and software stack; (iv) mobility of IoT devices; and (iii) the complex interplay between the IoT and edge layers. Unlike cloud computing, where researchers and developers seeking to test capacity planning, resource selection, network configuration, computation placement, and security management strategies had access to public cloud infrastructure (eg, Amazon and Azure), establishing an IoT and edge computing testbed that offers a high degree of verisimilitude is not only complex, costly, and resource‐intensive but also time‐intensive. Moreover, testing in real IoT and edge computing environments is not feasible due to the high cost and diverse domain knowledge required in order to reason about their diversity, scalability, and usability. To support performance testing and validation of IoT and edge computing configurations and algorithms at scale, simulation frameworks should be developed. Hence, this article proposes a novel simulator IoTSim‐Edge, which captures the behavior of heterogeneous IoT and edge computing infrastructure and allows users to test their infrastructure and framework in an easy and configurable manner. IoTSim‐Edge extends the capability of CloudSim to incorporate the different features of edge and IoT devices. The effectiveness of IoTSim‐Edge is described using three test cases. Results show the varying capability of IoTSim‐Edge in terms of application composition, battery‐oriented modeling, heterogeneous protocols modeling, and mobility modeling along with the resources provisioning for IoT applications. [ABSTRACT FROM AUTHOR]

Various commercial off-the-shelf components are available for the development of communication-enabled consumer electronics devices. This opens new doors to attackers who can take advantage of various vulnerabilities to attack the entire network and compromise the integrity of the system and the environment. If a malicious device enters the environment and the attacker gains access to the server or transmits malicious data to the server or cloud, the entire network can be jeopardized. To avoid such cases, this paper presents a device authentication scheme which uses physical unclonable functions (PUFs) and is suitable for the Internet-of-Medical-Things (IoMT). The main advantage of this authentication scheme is that no data related to the IoMT devices are stored in server memory. The time taken to authenticate the devices completely was 1.2 s to 1.5 s. A hybrid oscillator arbiter physical unclonable function was used for validation of the proposed authentication scheme. From the PUF module used during experimental validation, the number of keys that could be potentially used for the authentication protocol from each design is approximately 240. The proposed authentication scheme increases the robustness of the design while being lightweight to be deployed in various designs and supports scalability. [ABSTRACT FROM AUTHOR]

This article introduces the concept of proof of authentication (PoAh) for the lightweight implementation of blockchains in the Internet of Things (IoT). The PoAh can replace existing consensus algorithms, such as proof of work (PoW), proof of stake (PoS), and proof of activity (PoA), for resource- and energyconstrained infrastructures, such as the IoT. [ABSTRACT FROM AUTHOR]

Fog computing is a recent research trend to bring cloud computing services to network edges. EDCs are deployed to decrease the latency and network congestion by processing data streams and user requests in near real time. EDC deployment is distributed in nature and positioned between cloud data centers and data sources. Load balancing is the process of redistributing the work load among EDCs to improve both resource utilization and job response time. Load balancing also avoids a situation where some EDCs are heavily loaded while others are in idle state or doing little data processing. In such scenarios, load balancing between the EDCs plays a vital role for user response and real-time event detection. As the EDCs are deployed in an unattended environment, secure authentication of EDCs is an important issue to address before performing load balancing. This article proposes a novel load balancing technique to authenticate the EDCs and find less loaded EDCs for task allocation. The proposed load balancing technique is more efficient than other existing approaches in finding less loaded EDCs for task allocation. The proposed approach not only improves efficiency of load balancing; it also strengthens the security by authenticating the destination EDCs. [ABSTRACT FROM PUBLISHER]

Introducing the Special Topic on "Mitigating Cyber Threats and Defense in Data Intensive Smart Cities" For example, according to National Institute of Standards and Technology's International Technical Working Group on IoT-Enabled Smart City Framework[1]: I Two barriers currently exist to effective and powerful smart city solutions. Technologies are increasingly pervasive in our data intensive smart cities, as evidenced by the broad range of Internet-connected devices (also referred to as "Internet of Things" (IoT)) and systems, ranging from smart grids to intelligent systems and technology to critical information infrastructure. [Extracted from the article]

Data processing in real-time brings better business modeling and an intuitive plan of action. Internet of things (IoT), being a source of sensitive data collected and communicated through either public or private networks, requires better security from end to end to uphold integrity, quality, and acceptability of data. Designing an adaptive solution plays a vital role where IoT is deployed for the sensing-as-a-services in the critical infrastructure and near real-time decision making by deploying data analysis in the edge datacenters. Again, securing the system with user's demand and device specifications is a challenging and open research problem. This paper proposed a decision tree based user-centric security approach named D e c i s i o n T S e c that provides a secure channel for communication in IoT networks, combining edge datacenters in the network edges. Further, the proposed D e c i s i o n T S e c is validated by experimenting with the real-time testbed for the system performance along with the theoretical security validation. [ABSTRACT FROM AUTHOR]

Due to the various emerging technologies, an enormous amount of data, termed as Big Data, gets collected every day and can be of great use in various domains. Clustering algorithms that store the entire data into memory for analysis become unfeasible when the dataset is too large. Many clustering algorithms present in the literature deal with the analysis of huge amount of data. The paper discusses a new clustering approach called an Incremental Random Sampling with Iterative Optimization Fuzzy c-Means (IRSIO-FCM) algorithm. It is implemented on Apache Spark, a framework for Big Data processing. Sparks works really well for iterative algorithms by supporting in-memory computations, scalability, etc. IRSIO-FCM not only facilitates effective clustering of Big Data but also performs storage space optimization during clustering. To establish a fair comparison of IRSIO-FCM, we propose an incremental version of the Literal Fuzzy c-Means (LFCM) called ILFCM implemented in Apache Spark framework. The experimental results are analyzed in terms of time and space complexity, NMI, ARI, speedup, sizeup, and scaleup measures. The reported results show that IRSIO-FCM achieves a significant reduction in run-time in comparison with ILFCM. [ABSTRACT FROM AUTHOR]