Edge-cloud computing refers to a paradigm that combines the benefits of edge and cloud computing to optimize data processing and resource utilization. Edge-cloud computing plays a crucial role in resource allocation by optimizing the distribution of computational resources between edge devices and centralized cloud infrastructures. In the rapidly evolving landscape of edge-cloud computing, efficient VM allocation is critical for optimizing resource utilization, minimizing latency, and ensuring high SLA compliance. This paper introduces a novel heuristic VM allocation strategy, named LLCD, to enhance cloudlet or task processing in edge-cloud data centers. By employing a heuristic approach inspired by mixed-integer nonlinear programming models, this strategy dynamically assigns VMs based on their current load and the impending deadlines of tasks, significantly reducing overall system latency and enhancing SLA success rates. Simulation was conducted across various computational intensities. The findings reveal that the proposed approach substantially improves resource utilization and operational efficiency, adapting to dynamic workloads, by achieving an SLA success ratio as 74.26% and 83.7% in different deadline scenarios. The adaptive nature of the LLCD algorithm allows real-time task reallocation based on system feedback, which mirrors the operational principles of AI-driven orchestration in distributed IoT environments. The validation is achieved through a multi-iteration simulation model that emulates dynamic IoT workloads, demonstrating LLCD’s learning capability in maintaining SLA stability and consistent latency reduction across changing task distributions. Moreover, the proposed heuristic provides a foundation for latency-efficient and learning-based management in distributed computing environments.

Nanorobots are microscopic robots that operate at the molecular and cellular level and can potentially revolutionize fields such as medicine, manufacturing, and environmental monitoring due to their precision. However, the challenge for researchers is to analyze the data and provide a constructive recommendation framework instantly, as most nanorobots demand on-time and near-edge processing. To tackle this challenge, this research presents a novel edge-enabled intelligent data analytics framework called Transfer Learning Population Neural Network (TLPNN) to predict glucose levels and associated symptoms from invasive and non-invasive wearable devices. The TLPNN is designed to be unbiased in predicting symptoms during the initial phase but later modified based on the best-performing neural networks during the learning phase. The effectiveness of the proposed method is validated using two publicly available glucose datasets with various performance metrics. The simulation results demonstrate the effectiveness of the proposed TLPNN method over existing ones.

Blockchain has gained increasing attention for managing eHealth data, yet most existing ledger designs face fundamental challenges in simultaneously enforcing strict privacy protections, enabling low-latency data queries, and supporting transparent AI-driven decision-making. This paper proposes a novel blockchain ledger architecture integrating Explainable Artificial Intelligence (XAI) through SHapley Additive exPlanations (SHAP) and statistical metadata for privacy-preserving and efficient query processing in eHealth applications. Our method introduces dynamic, expert-informed sensitivity classification and interpretable SHAP values directly embedded in block headers. The integration significantly reduces query data requirements by approximately 99.78%, maintaining patient data privacy through role-based access control and metadata-driven querying. Empirical validation in an IoMT-enabled healthcare scenario confirms substantial query efficiency, preserving privacy and transparency improvements, justifying the computational overhead associated with metadata creation. The results highlight the practical utility and robustness of our proposed blockchain architecture for rapid clinical decision-making.

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.

This paper introduces a significant advancement in the Proof of Authentication (PoAh) consensus algorithm, designed specifically for resource-constrained Internet of Things (IoT) devices. Building upon the foundations of PoAh consensus, this enhanced iteration, known as PoAh 2.0, integrates Artificial Intelligence (AI) at the block creator node level. This novel approach allows for the generation of block transactions embedded with AI-determined sensitivity and other applicable transaction-related metadata, a pioneering concept in this domain. The verifier node, a trusted entity, is tasked with verifying incoming blocks, utilizing the block header and its metadata information to determine authenticity while preserving the privacy of the content of the block’s data. A core innovation of PoAh 2.0 is its dynamic authentication mechanism, which adapts to the sensitivity level of the data within each block, behaving in an adaptive way based on the situation. AI plays a crucial role in this process, ensuring the block’s integrity and security are maintained. To demonstrate the efficacy of this advanced AI-enabled PoAh 2.0 consensus, we conducted a case study in an Internet of Medical Things (IoMT)-based eHealth scenario. The results from this study reveal that our developed dynamic authentication technique not only significantly enhances the original PoAh version but also establishes a new benchmark in block validation and security for eHealth applications. The integration of AI and improved dynamic authentication, calibrated to the security needs of each block, marks a novel and significant stride in blockchain research. This development not only enriches the current understanding of blockchain applications in IoT, but also sets a new direction for future research in secure and efficient blockchain implementations in the IoMT-Edge centric eHealth landscape.

The rapid integration of IoT devices into everyday decision-making processes underscores the need for continuous user authentication and data integrity checking during network communication, all while minimizing energy consumption to extend device lifespan. This paper introduces the Bio-Integrated Hybrid TESLA protocol, which is a fully symmetric and energy-efficient authentication protocol designed for resource-constrained IoT devices. Based on the Hybrid TLI-lTESLA protocol, this innovative solution prioritizes high cybersecurity levels and minimal computational requirements for continuous authentication. An innovative advancement involves eliminating the public cryptography process during the synchronization stage of TESLA protocols. Instead, biometric authentication through distorted fingerprint and EEG templates is employed, to establish a non-shared symmetric session key, utilized only once. Furthermore, neither the key nor the original biometric templates are transmitted over the network, ensuring user identity preservation and effectively resolving the key distribution challenge inherent in symmetric cryptography. By offloading intensive tasks to servers and avoiding the storage or transmission of biometric data, the proposed approach conserves IoT device energy and enhances cybersecurity. Simulation analyses and cybersecurity assessments demonstrate successful synchronization, privacy preservation, and low computational demands compared to existing protocols, making the Bio-Integrated Hybrid TESLA protocol a significant advancement in IoT authentication.

Machine Learning (ML) and Artificial Intelligence (AI) can harness the immense amount of operational data from clouds to services, to social and communication networks. In the era of data science and connected devices of all varieties, Intelligence have found ways to improve operations and management of next generation networks, systems, and services. Further research is therefore needed to understand and improve the potential and suitability of ML/AI in the context of network, system, and service operations and management. This will provide deeper understanding and better decision making based on largely collected and available operational and management data. It will also present opportunities for improving ML/AI algorithms on aspects such as reliability, dependability, and scalability, as well as demonstrate the benefits of these methods in control and management systems. Moreover, there is an opportunity to define novel platforms that can harness the vast operational data and advance ML/AI algorithms to drive management decisions in open and highly programmable networks, clouds, and data centers.

To facilitate the Edge AI paradigm in distributed networks, we propose novel collaborative learning methodologies for a connected network of edge nodes. Our proposed methodologies tackle the challenges in distributed learning where there are constraints on data privacy and a low degree of overlap between the classes observed by the nodes. These approaches entail sharing class distribution information between nodes, computing nodes, and class weights, training local models on each node, then aggregating the models using the determined weights. It favors nodes that have encountered unique or less common classes in their local datasets. Through a series of experiments using an activity recognition dataset, we demonstrate the effectiveness and scalability of our proposed approaches. We show the adaptive nature of the proposed approach by achieving classification accuracy above the baseline, even with little overlap between the observed classes. This study serves as a foundation for future advancements in collaborative learning on edge networks, and encourages the development of scalable solutions.

This article provides in-depth experimental studies of XAI (EXplainable Artificial Intelligence) in the IoT-Edge-Cloud continuum. Within the different available XAI frameworks, such as Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) frameworks are utilized here as they are the most suitable feature map-based, model-agnostic, posthoc frameworks that match our requirements for getting real-time prediction explanations in the healthcare domain. In order to evaluate LIME and SHAP in this continuum and to make black box AI (BBAI)-based decisions interpretable, we have considered the real-world electronic health record (EHR)-based large cloud database (which could be a very large database–VLDB) and IoMT based real-time streams as edge databases for the prediction of cardiac arrest in the real-world. We have also verified the effectiveness of automated counterfactual explanations in this context for taking remedial actions. Thus, our proposed model is capable of making significant advancements in the healthcare industry by offering conscious healthcare monitoring automation along with an AI-based self-explanatory system that serves as a personalized health assistant for individuals, paving the way for the next major upgrade in healthcare.

Collaborative Edge Computing (CEC) works on the distributed model, and is established at the Fog layer that consists of multiple edge devices like Edge Data Centers (EDCs), Edge Routers etc. In the CEC environment, the Edge layer has the capability of storing and processing data. Since the processing capacity is limited, many edge devices collaborate with each other to offload the processing in a scheme called Load Balancing. CEC enables applications in smart villages through task offloading/sharing, which calls for a trusted security system to make the resource sharing and information safe. Since the Edge is a resource-constrained environment where not all data centers are resourceful enough to implement computation intensive security systems. Physically Unclonable Functions (PUF) are a robust, secure, and light-weight solution for providing hard- ware security. PUFs are used to authenticate the EDCs during load balancing in a collaborative edge computingenvironment. Though PUFs are secure and difficult to remodel, the drawback lies in the storage of Challenge-Response Pairs (CRP) in a CRP database. The storage space for the CRP database becomes a concern when many EDCs participate in dynamic load balancing and each EDC needs to store a copy of the database. This research proposes a PUF based certificate Authority protocol for authentication of EDCs which will eliminate the need for CRP database storage while harnessing the security feature of the PUF. Further, in this research the effised authentication system is evaluated through oretical analysis and experimental results.

Machine learning and artificial intelligence can harness the immense stream of operational data from clouds, to services, to social and communication networks. In the era of big data and connected devices of all varieties, machine learning and artificial intelligence have found ways to improve operations and management of information technology and communications.

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.

Summary Advancement of computing and communication techniques transforms the traditional transport system into the intelligent transportation system (ITS). The development of distributed computing in a vehicular network platform also called Vehicular Edge Computing (VEC) promise to address most of the challenges faced by the ITS. Localization is important in these vehicular networks because of its key contribution in autonomous driving, smart traffic monitoring, and collision avoidance services. For localization, current GPS and hybrid methods are in‐efficient because of GPS outage in urban infrastructure and dynamic nature of the vehicular networks. The cooperative localization approaches, on the other hand, use dedicated short range communication to broadcast messages and estimate location. However, these messages are un‐encrypted and periodic which gives a privacy risk for vehicles. This article presents a privacy‐preserving cooperative localization in vehicular network based upon dynamic pseudonym changing strategy. First, the localization delay is addressed with the implementation of dynamic vehicular edge assignment for computational task management. In the next step, the localization is estimated from the neighbor and road side unit ranging measurement followed by a real‐time prediction of the vehicle. The performance of the proposed algorithms is analyzed in terms of localization accuracy and privacy preservation strength. Furthermore, the proposed method is simulated in a real city scenario followed by localization accuracy and privacy analysis. Finally, the localization accuracy and privacy strength of the proposed approach are compared with the state‐of‐the‐art methods.

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.

Machine learning and artificial intelligence can harness the immense stream of operational data from clouds, to services, to social and communication networks. In the era of big data and connected devices of all varieties, machine learning and artificial intelligence have found ways to improve operations and management of information technology and communications.

While being a lasting theme, privacy, security, and trust (PST) has been increasingly important in recent days due to the pervasive (but more prone) computation infrastructure and deep (but more intrusive) data analytics, and has been hugely demanded from governments, companies, and individuals. This workshop aims at providing a forum for researchers, practitioners and developers from different background areas such as computational intelligence, data privacy and cyber security, trust management, cloud computing, edge computing, Internet of Things, big data analytics, machine learning and data mining, knowledge discovery to exchange the latest experience, research ideas and synergic research and development on fundamental issues and applications about privacy, security and trust issues in computational intelligence.

To handle the fuzziness and spatial uncertainties among pixels entailed in color images, this paper proposes a novel fuzzy entropy function for multi-threshold image segmentation based on the energy curve concept and minimum fuzzy entropy criterion. The proposed energy curve based new fuzzy entropy function (ECFE) considers intensity distribution and spatial contextual information among the pixels. To improve efficiency and threshold selection process of the method, cuckoo search algorithm is employed. For comparison, backtracking search algorithm, and Lévy flight based firefly algorithm included. Comparison with recent color image multilevel segmentation techniques presented to test the effectiveness of the proposed algorithm. The performance of the proposed technique is evaluated using different satellite and natural color images. Quantitative and qualitative results demonstrate that the proposed algorithm is highly accurate, robust, and efficient for color image multilevel segmentation.

SLA violations do happen in real world. An SLA violation represents the failure of guaranteeing a service, which leads to unwanted consequences such as penalty payments, profit margin reduction, reputation degradation, customer churn and service interruptions. Hence, in the context of cloud-hosted big data analytics applications (BDAAs), it is paramount for providers to predict and prevent SLA violations. While machine learning-based techniques have been applied to detect SLA violations for web service or general cloud service, the study on detecting SLA violations dedicated for cloud-hosted BDAAs is still lacking. In this article, we propose four machine learning techniques and integrate 12 resampling methods to detect SLA violations for batch-based BDAAs in the cloud. We evaluate the efficiency of the proposed techniques in comparison with ideal and baseline classifiers based on a real-world trace dataset (Alibaba). Our work not only helps providers to choose the best performing prediction technique, but also provides them capabilities to uncover the hidden pattern of multiple configurations of BDAAs across layers.

Industry 4.0 have automated the entire manufacturing sector (including technologies and processes) by adopting Internet of Things and cloud computing. To handle the workflows from Industrial Cyber-Physical systems, more and more data centers have been built across the globe to serve the growing needs of computing and storage. This has led to an enormous increase in energy usage by cloud data centers, which is not only a financial burden but also increases their carbon footprint. The private software defined wide area network (SDWAN) connects a cloud provider's data centers across the planet. This gives the opportunity to develop new scheduling strategies to manage cloud providers workload in a more energy-efficient manner. In this context, this article addresses the problem of scheduling data-driven industrial workflow applications over a set of private SDWAN connected data centers in an energy-efficient manner while managing tradeoff of a cloud provider' revenue. Our proposed algorithm aims to minimize the cloud provider's revenue and the usage of nonrenewable energy by utilizing the real-world electricity prices with the availability of green energy on different cloud data centers, where the energy consumption consists of the usage of running application over multiple data centers and transferring the data among them through SDWAN. The evaluation shows that our proposed method can increase usage of green energy for the execution of industrial workflow up to 3× times with a slight increase in the cost when compared to cost-based workflow scheduling methods.

Today's vehicles are advancing from stand-alone transportation means to vehicle-to-vehicle, and vehicle-to-infrastructure communications enabled devices which are able to exchange data through the transportation communication infrastructure. As the IoT and data remain intrinsically linked together, the fast-changing mobility landscape of intent-based networking for the Internet of connected vehicles comes with a great risk of data security and privacy violations. This paper considers the privacy issues in the distributed edge computing, in which the data is communicated between a number of vehicles in the IoT layer and potentially untrusted edge controllers at the edge of the network. The sensory data communicated by the vehicles contain sensitive information, such as location and speed, which could violate the users' privacy if they are leaked with no perturbation. Recent studies suggest mechanisms for randomizing the stream of data to ensure individuals' privacy. Although the past works on differential privacy provide a strong privacy guarantee, they are limited to applications where communication parties are trusted and/or there is no correlation between the users or the featured of sensory data. In this paper, we address this gap by proposing a differentially private data streaming system that adds a correlated noise in the vehicle's side (IoT layer) rather than the transportation infrastructure. Also, our system is able to ensure a strong privacy level over time. The proposed mechanism is data-adaptive and scales the noise with respect to the data correlation. Our extensive experiments demonstrate that the utility of the output generated by our method outperforms the recent approaches.

The localization accuracy is critical for the development of future autonomous systems and location-based services. The accuracy level for localization is difficult to achieve in the case of urban and GPS denied environments due to high scattering. Fingerprint-based localization techniques promise to address these challenges. However, this technique demands to build a radio map before localization, which is a time-consuming and labor-intensive task. This article designs a crowd-sourced based localization system to address the radio map building problem in fingerprinting localization system. In this method, the first initial radio map is constructed from the path-loss RSS model, followed by the update of the fingerprints with crowd-sourcing. Finally, the vehicle location is estimated from the RSS sample by matching it with an updated radio map with a deep learning algorithm. The main advantage of the proposed approach is the calibration-free crowd-sourced fingerprint generation and its applicability in various location-based services in urban infrastructure.

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.

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 attacks may further open scope for many 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.

The Internet has resulted in cyber-threats and cyber-crimes, which can occur anywhere at any time. Among various cyber threats, modern malware with applied metamorphosis and polymorphic technology is a concern as it can proliferate to advanced variants from its original shape. The typical malware analysis methods, including signature-based approach, remain vulnerable to such advanced variants. This paper proposes a visualization-based approach for malware analysis using the state-of-the-art Convolution Neural Network (CNN) model such as ResNeXt, which had achieved outstanding performance in image classifications with competitive computational complexity. The proposed method transforms the attributes of raw malware binary executable files to greyscale images for further analysis by well-established deep learning models. The greyscale images, which result of data transformation for visualization, are classified using ResNeXt. The experiment results show that the proposed solution achieves 98.32% and 98.86% of accuracy in malware classification on Malimg dataset and modified Malimg dataset, respectively. The proposed method outperforms other comparable methods in terms of classification accuracy and requires similar level of computational power.

The integration of Internet of Things (IoT) and software defined networks is the most suitable network paradigm for development of smart world. IoT based solutions have been developed for fulfilling the gap between Cyber and physical world. There are many issues for realizing the IoT due to its large scale and heterogeneous network structure. Energy efficient node deployment also called green deployment for IoT is one of the major challenging issue. Hence most of the existing deployment methods for WSNs are not workable for IoT. This paper addresses the challenges of deployment schemes to get an energy efficient IoT networks. It presents a homogeneous grid based deployment strategy and a heterogeneous circle packing based deployment strategy. The performance of both approaches are calculated by simulating different deployment scenarios. The network lifetime and energy consumption are calculated. It is found that the circle packing based deployment approach outperforms the other grid based approach in terms of energy efficiency and well suited for the heterogeneous network.

In software-defined networks (SDNs), the deployment of multiple controllers improves the reliability and scalability of the distributed control plane. Recently, edge computing (EC) has become a backbone to networks where computational infrastructures and services are getting closer to the end user. The unique characteristics of SDN can serve as a key enabler to lower the complexity barriers involved in EC, and provide better quality-of-services (QoS) to users. As the demand for IoT keeps growing, gradually a huge number of smart devices will be connected to EC and generate tremendous IoT traffic. Due to a huge volume of control messages, the controller may not have sufficient capacity to respond to them. To handle such a scenario and to achieve better load balancing, dynamic switch migrating is one effective approach. However, a deliberate mechanism is required to accomplish such a task on the control plane, and the migration process results in high network delay. Taking it into consideration, this article has introduced an efficient switch migration-based load balancing (ESMLB) framework, which aims to assign switches to an underutilized controller effectively. Among many alternatives for selecting a target controller, a multicriteria decision-making method, i.e., the technique for order preference by similarity to an ideal solution (TOPSIS), has been used in our framework. This framework enables flexible decision-making processes for selecting controllers having different resource attributes. The emulation results indicate the efficacy of the ESMLB.

Internet of Multimedia Things (IoMT) applications generate huge volumes of multimedia data that are uploaded to cloud servers for storage and processing. During the uploading process, the IoMT applications face three major challenges, i.e., node management, privacy-preserving, and network protection. In this article, we propose a multilayer framework (PAAL) based on a multilevel edge computing architecture to manage end and edge devices, preserve the privacy of end-devices and data, and protect the underlying network from external attacks. The proposed framework has three layers. In the first layer, the underlying network is partitioned into multiple clusters to manage end-devices and level-one edge devices (LOEDs). In the second layer, the LOEDs apply an efficient aggregation technique to reduce the volumes of generated data and preserve the privacy of end-devices. The privacy of sensitive information in aggregated data is protected through a local differential privacy-based technique. In the last layer, the mobile sinks are registered with a level-two edge device via a handshaking mechanism to protect the underlying network from external threats. Experimental results show that the proposed framework performs better as compared to existing frameworks in terms of managing the nodes, preserving the privacy of end-devices and sensitive information, and protecting the underlying network.

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.

The Internet of Things (IoT) is becoming a backbone of sensing infrastructure to several mission-critical applications such as smart health, disaster management, and smart cities. Due to resource-constrained sensing devices, IoT infrastructures use Edge datacenters (EDCs) for real-time data processing. EDCs can be either static or mobile in nature, and this article considers both of these scenarios. Generally, EDCs communicate with IoT devices in emergency scenarios to evaluate data in real-time. Protecting data communications from malicious activity becomes a key factor, as all the communication flows through insecure channels. In such infrastructures, it is a challenging task for EDCs to ensure the trustworthiness of the data for emergency evaluations. The current communication security pattern of “communication before authentication” leaves a “black hole” for intruders to become part of communication processes without authentication. To overcome this issue and to develop security infrastructures for IoT and distributed Edge datacenters, this article proposes a user-centric security solution. The proposed security solution shifts from a network-centric approach to a user-centric security approach by authenticating users and devices before communication is established. A trusted controller is initialized to authenticate and establishes the secure channel between the devices before they start communication between themselves. The centralized controller draws a perimeter for secure communications within the boundary. Theoretical analysis and experimental evaluation of the proposed security model show that it not only secures the communication infrastructure but also improves the overall network performance.

Improving quality of service (QoS) of low power and lossy networks (LLNs) in Internet of things (IoT) is a major challenge. Cluster-based routing technique is an effective approach to achieve this goal. This paper proposes a QoS-aware clustering-based routing (QACR) mechanism for LLNs in Fog-enabled IoT which provides a clustering, a cluster head (CH) election, and a routing path selection technique. The clustering adopts the community detection algorithm that partitions the network into clusters with available nodes' connectivity. The CH election and relay node selection both are weighted by the rank of the nodes which take node's energy, received signal strength, link quality, and number of cluster members into consideration as the ranking metrics. The number of CHs in a cluster is adaptive and varied according to a cluster state to balance the energy consumption of nodes. Besides, the protocol uses the CH role handover technique during CH election that decreases the control messages for the periodic election and cluster formation in detail. An evaluation of the QACR has performed through simulations for various scenarios. The obtained results show that the QACR improves the QoS in terms of packet delivery ratio, latency, and network lifetime compared to the existing protocols.

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 SDN-assisted 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.

A major concern in distributed networks is the ability to provide acceptable levels of security. This is achieved by using encryption and authentication mechanisms that depend on encryption keys. However, given the ever-expanding nature of the network, it is difficult to keep setting up authorities that can aid the key- exchange process. This paper presents a novel solution to the challenge of exchanging keys of a large, distributed network without the need to set up additional authorities. The key-exchange scheme presented takes advantage of features such as packet anonymity, random selection and a multi- path approach for the exchange process. The paper also discusses the effectiveness of the proposed scheme against various threat scenarios.

With the rapid development and increasing complexity of computer systems and communication networks, user requirements for trust, security, and privacy are becoming more and more demanding. Therefore, there is a grand challenge that traditional security technologies and measures may not meet user requirements in open, dynamic, heterogeneous, mobile, wireless, and distributed computing environments. Thus, there is a strong need to build systems and networks in which various applications allow users to enjoy more comprehensive services while preserving trust, security, and privacy at the same time. As useful and innovative technologies, trusted computing and communications are attracting researchers with more and more attention. The scope of this special issue is broad and is representative of many important topics involving emerging technologies in the field of Trust, Security, Privacy, Forensics, and Data analytics. In addition, the articles selected through this special issue also present strong aspects on theoretical analysis, algorithms, and practical experience in their proposed schemes. The submissions to the Special Issue were significantly extended research papers from the 16th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (Trustcom 2017). This conference brings together researchers and practitioners around the world working on trusted computing and communications, with regard to trust, security, privacy, reliability, dependability, survivability, availability, and fault tolerance aspects of computer systems and networks. All the submissions for this special issue have been reviewed rigorously following the guidelines of Wiley Journal on Concurrency and Computation: Practice and Experience (CCPE). A majority of the reviewers represent expertise in their fields who provided high quality reviews for the manuscripts. The articles selected through a rigorous reviews process for this Special Issue are briefly presented in the rest of this guest editorial. The guest editors sincerely believe that this special issue on Trust, Security, and Privacy will be a great reading for the contemporary researchers worldwide. The guest editors would like to thank the editor-in-chief (EiC) Dr Geoffrey Fox of the Wiley Journal on Concurrency and Computation: Practice and Experience (CCPE) for the opportunity of this special issue. The guest editors are sincerely thankful to the many reviewers around the globe for their timely reviews without which this successful special issue would not have been possible. The guest editors thank the authors for their patience and dedication at all stages of the review process. The guest editors are also thankful to the Wiley production staffs for their help during the production of this special issue. Fault injection has been increasingly used both to attack software applications and to test system robustness. Detecting fault injection vulnerabilities has been approached with a variety of different but limited methods. Given-Wilson et al1 propose extension of a recently published general model checking–based process to detect fault injection vulnerabilities in binaries. This new extension makes the general process scalable to real-world implementations. The authors demonstrate their scheme by detecting vulnerabilities in different cryptographic implementations. Fault analysis of AEZ is based on AES using three 128-bit keys. Al Mahri et al2 analyzed AEZ 4.2 and investigated the fault issue showing all three 128-bit keys used in AEZ 4.2 can be uniquely retrieved using only three random valued single byte fault injections. Data publishing may suffer from privacy disclosures, especially, the case in transactional data such as web search and point of sales logs. Current potent privacy preserving mechanisms mainly focus on relational data. Bewong et al3 propose a new privacy metric for transactional data to prevent inference attacks. Their proposed scheme, Anony, ensures that the adversary learns no more about an intended victim than what is publicly available. In order to demonstrate the effectiveness of their scheme, the authors present empirical evaluation on three benchmark datasets. Jahan et al4 present selective read/write access to the outsourced data for clients using mobile devices supporting users from multiple domains. The authors use Ciphertext-Policy Attribute-based Encryption (CP-ABE) scheme that provides access control on encrypted outsourced data. The proposed scheme provides fine-grained read/write access to the users, accompanied with a lightweight signature scheme and computationally inexpensive user revocation mechanism suitable for resource-constrained mobile devices. Both theoretical analyses of the security protocol and experimental results measured from a real-world testbed strongly validate the proposed scheme. Yoking-proof scheme is a very useful mechanism in many IoT (Internet of Things) application areas such as health care and supply chain. However, existing yoking-proof scheme requires two or more rounds of communication to generate the yoking-proof. Sun et al5 investigate how to design the one-round yoking-proof scheme with computational efficiency. The scheme is designed with a new timestamp-based scheme for the RFID tag pair. The authors prove the security and privacy of the proposed scheme extending to more than two RFID tags along with one-round of communication to generate the yoking-proof. While Malware-based activities on recent years are slowing down, more and more sophisticated targeting malwares have been emerging. These new categories of Malwares share little or no common feature with traditional malware. Han et al6 present classifications of malicious tasks using decidable theory and prove that tasks performed by any software can be recursive and determinable. By establishing a mapping from software to task, they prove their proposition and demonstrate that presence of malwares in software is recursive. This issue would be incomplete without the article on IoT security. Secured authentication using 6LoWPAN networks is one of the important considerations among various IoT-based applications. Existing asymmetric key distribution scheme may not be a perfect choice as recent research shows that Lucky Thirteen attack has compromised Datagram Transport Layer Security (DTLS) with Cipher Block Chaining (CBC) mode for key establishment. Even though EAKES6Lo and S3 K techniques for key establishment follow the symmetric key establishment method, they strongly rely on a remote server and trust anchor. Baskaran et al7 present a Lightweight AUthentication Protocol (LAUP) using symmetric key method with no pre-shared keys between sensors and Edge Router in a 6LoWPAN environment. Their proposed scheme is formally verified using the Scyther security protocol verification tool and the protocol is implemented using COOJA simulator. Finally, the authors develop a Testbed to measure computation time and efficiency of LAUP scheme. The proposed scheme achieves less computational time and low power consumption compared to existing authentication protocols such as the EAKES6Lo and SAKES.

The sybil attack in Internet of Things (IoT) commonly aims the sensing domain that may impose serious threat to the devices both in perception and communication layer. The singularity of the sybil attack is a sybil node that publish multiple identities of legitimate devices. It is highly essential to learn the behavior and predict possible actions of a sybil attacker while devising a defense mechanism for it. This paper provides a comprehensive characteristic analysis of sybil attack in IoT. Based on the nature of the task performed during this attack, it is classified into three phases as compromise, deployment, and launching phase. The compromise phase is modeled as an automaton with attacker state transition as a Markov chain model. A heuristic is also proposed for selection criteria of an attacker to compromise a node. In the deployment phase of the attack, an algorithm based on K -mean clustering is proposed to group compromised identities and deploy the sybil node for corresponding identities without violating the set of adjacent nodes. In the launching phase, the process of replacing sybil identities either over time or on detection is modeled using age replacement policy. The results depict that the proposed model effectively visualize the behavior of a sybil attacker in challenging environments of IoT.

Internet of Things (IoT) provides a promising opportunity to build powerful data analytics systems with real time event detection for smart health, and therefore wearable IoT has become a rising source of big data streams for smart health, for which security needs to be assured by detecting real-time event to avoid malicious activities, and meanwhile to control the information leakage of big sensing data streams. I refer to this as an information flow control problem. To address this problem, this paper proposes a static lattice model for information flow control over big sensing data streams. I initialize two static lattices, i.e., sensor lattice for wearable sensors and user lattice for users, and then static lattices aim to process the flow control model faster, because I am dealing with high volume and velocity of data streams. The experimental evaluation and results of the information flow model show that it can excellently handle the incoming big data streams with low latency and buffer requirement.

Edge computing is an emerging research area to incorporate cloud computing into edge network devices. An Edge datacenter, also referred to as EDC, processes data streams and user requests in real-time and is therefore used to decrease the latency and congestion in the network. EDC is usually setup as a distributed system and is accordingly placed between the cloud datacenter and the data source . These EDCs work as an intermediate layer in the fog hierarchy between IoT and Cloud datacenter. EDC’s are aided by load balancers, responsible for distributing the workload amongst multiple EDC, in order to optimize resource utilization and response time . The load balancers make sure that the workload is equally divided amongst the available EDCs to avoid over loading of some EDCs while other remain idle as this directly impacts the user response and real-time event detection . Given the fact that EDCs are deployed in remote environments, the need for secure authentication is of major importance. In this paper we propose a novel load balancing technique that enables EDC authentication as well as identification of idle EDCs for better load balancing. The proposed load balancing technique is also compared with existing approaches and proves to be more efficient in locating EDC’s with less workload. In addition to the improved efficiency, the proposed scheme also strengthens the security of the network by incorporating destination EDC authentication.

We are pleased to announce the publication of the special issue focusing on intrusion detection and prevention in cloud, fog, and Internet of Things (IoT).Internet of Things (IoT), cloud, and fog computing paradigms are as a whole provision a powerful large-scale computing infrastructure for many data and computation intensive applications.Specifically, the IoT technologies and deployment can widely perceive our physical world at a fine granularity and generate sensing data for further insight extraction.The fog computing facilities can provide computing power near the IoT devices where data are generated, aiming to achieve fast data processing for time critical applications or save the amount of data transmitted into cloud for storage or further processing.The cloud computing platforms can offer big data storage and large-scale processing services for cheap long-term storage or data intensive analytics with more advanced data mining models.Hence, it can be seen that the IoT/fog/cloud computing infrastructures can support the whole lifecycle of large-scale applications where big data collection, transmission, storage, processing, and mining can be seamlessly integrated.However, these state-of-the-art computing infrastructures still suffer from severe security and privacy threats because of their built-in properties such as the ubiquitous-access and multitenancy features of

Resource constrained sensing devices are being used widely to build and deploy self-organizing wireless sensor networks for a variety of critical applications such as smart cities, smart health, precision agriculture and industrial control systems. Many such devices sense the deployed environment and generate a variety of data and send them to the server for analysis as data streams. A Data Stream Manager (DSM) at the server collects the data streams (often called big data) to perform real time analysis and decision-making for these critical applications. A malicious adversary may access or tamper with the data in transit. One of the challenging tasks in such applications is to assure the trustworthiness of the collected data so that any decisions are made on the processing of correct data. Assuring high data trustworthiness requires that the system satisfies two key security properties: confidentiality and integrity. To ensure the confidentiality of collected data, we need to prevent sensitive information from reaching the wrong people by ensuring that the right people are getting it. Sensed data are always associated with different sensitivity levels based on the sensitivity of emerging applications or the sensed data types or the sensing devices. For example, a temperature in a precision agriculture application may not be as sensitive as monitored data in smart health. Providing multilevel data confidentiality along with data integrity for big sensing data streams in the context of near real time analytics is a challenging problem. In this paper, we propose a Selective Encryption (SEEN) method to secure big sensing data streams that satisfies the desired multiple levels of confidentiality and data integrity. Our method is based on two key concepts: common shared keys that are initialized and updated by DSM without requiring retransmission, and a seamless key refreshment process without interrupting the data stream encryption/decryption. Theoretical analyses and experimental results of our SEEN method show that it can significantly improve the efficiency and buffer usage at DSM without compromising the confidentiality and integrity of the data streams.

In recent years, Industrial Wireless Sensor Network (IWSN) is gaining more popularity due to many applications in industries like fire detection, hazardous gas leakage detection, temperature monitoring, localization of sensors, etc. However, faulty sensors in the network may degrade the performance of the applications. In this paper, a software defined network (SDN) based fault detection method is proposed for IWSN. In this method, SDN plays an important role for controlling the whole system by setting a fault detection algorithm at the cluster heads (CHs). The CH periodically receives the monitoring data from the sensors and follows the fault detection algorithm set by the SDN to detect the faulty sensors in the network. The fault detection algorithm uses a statistical trimean method to detect the faulty sensors. Simulation results show that our proposed method performs better than Ji's fault detection method in terms of detection accuracy (DA) and false alarm rate (FAR). A IWSN prototype is also designed to evaluate the performance of the proposed method.

The use of cryptography systems in cyber security domain has become a primary focus to maintain data confidentiality. Several cryptography solutions exist for protecting data against confidentiality attack. Due to the advancement of computing infrastructure, there is always a need for novel security solution to protect data and introduce more complexity to the intruder. In this paper, a novel graph-based crypto-system is proposed to provide data confidentiality during communication between users and devices. The proposed crypto- system uses a set of graphs of order n along with an operation defined over it to form a group algebraic structure. Using this group, plaintext, ciphertext, and secret key are represented as a graph. The encryption and decryption processes are performed over the graphs using the operation defined in the group. It is then demonstrated that the proposed crypto-system is valid, and for a large n value, brute-forcing attempts to derive the key from plaintext or ciphertext is computationally infeasible.

Mobile cloud computing has emerged as a pervasive paradigm to execute computing tasks for capacity- limited mobile devices. More specifically, at the network edge, the resource-rich and trusted cloudlet system is acting as a 'data center in a box' to support compute-intensive mobile applications. The mobile cloudlets can provide in-proximity services by executing the workloads for nearby devices. Nevertheless, load balancing in mobile cloudlet network is of great importance, as it has a huge impact on task response time. Existing methods for cloudlet load balancing basically rely on the strategic placement or user cooperation. However, the above solutions require the global task load information from the whole network, which is costly in both communication and computation. To achieve more efficient and low-cost load balancing, we propose 'CTOM', a Collaborative Task Offloading Mechanism for mobile cloudlet networks. Our solution is based on the balls-and-bins theory and can balance the task load only requiring limited information. Extensive simulations and evaluation based on mobility trace demonstrate that, our CTOM outperforms the conventional random and proportional allocation schemes by reducing the task gaps among mobile cloudlets by 65% and 55% respectively. Meanwhile, CTOM's performance is close to that of the greedy algorithm but with much lower computing complexity.

Reducing energy consumption in the fog computing environment is both a research and an operational challenge for the current research community and industry. There are several industries such as finance industry or healthcare industry that require a rich resource platform to process big data along with edge computing in fog architecture. As a result, sustainable computing in a fog server plays a key role in fog computing hierarchy. The energy consumption in fog servers depends on the allocation techniques of services (user requests) to a set of virtual machines (VMs). This service request allocation in a fog computing environment is a nondeterministic polynomial-time hard problem. In this paper, the scheduling of service requests to VMs is presented as a bi-objective minimization problem, where a tradeoff is maintained between the energy consumption and makespan. Specifically, this paper proposes a metaheuristic-based service allocation framework using three metaheuristic techniques, such as particle swarm optimization (PSO), binary PSO, and bat algorithm. These proposed techniques allow us to deal with the heterogeneity of resources in the fog computing environment. This paper has validated the performance of these metaheuristic-based service allocation algorithms by conducting a set of rigorous evaluations.

Applications in risk-critical domains such as emergency management and industrial control systems need near-real-time stream data processing in large-scale sensing networks. The key problem is how to ensure online end-to-end security (e.g., confidentiality, integrity, and authenticity) of data streams for such applications. We refer to this as an online security verification problem. Existing data security solutions cannot be applied in such applications as they cannot deal with data streams with high-volume and high-velocity data in real time. They introduce a significant buffering delay during security verification, resulting in a requirement for a large buffer size for the stream processing server. To address this problem, we propose a Dynamic Key-Length-Based Security Framework (DLSeF) based on a shared key derived from synchronized prime numbers; the key is dynamically updated at short intervals to thwart potential attacks to ensure end-to-end security. Theoretical analyses and experimental results of the DLSeF framework show that it can significantly improve the efficiency of processing stream data by reducing the security verification time and buffer usage without compromising security.