Heterogeneous Internet Of Things Applications Research Articles

A hybrid meta-heuristic algorithm for multi-objective IoT service placement in fog computing environments

The fog computing paradigm is promising for deploying various delay-sensitive Internet of Things (IoT) applications. The resource-constrained fog devices restrict the number of application deployments due to a lack of efficient resource estimation and discovery mechanisms for various emergent heterogeneous IoT applications. An efficient resource allocation strategy is one of the best choices to meet these application’s Quality of Service (QoS) requirements and improve system performance. However, finding the best allocation strategy for IoT applications with more than one QoS parameter is a challenge, and it has been proved as a non-deterministic polynomial time (NP)-complete problem. This article formulates a classical weighted multi-objective IoT service placement to optimize three parameters, i.e., makespan, cost, and energy. The non-convexity nature of the solution space motivates us to focus on the population-based meta-heuristic algorithm, i.e. Genetic Algorithm (GA), Simulated Annealing (SA) and Particle Swarm Optimization (PSO), along with their combination GA-SA, and GA-PSO. It implements the algorithm and compares it with the greedy-based random placement approach, varying the number of IoT applications with different parameters. The final results reveal that the hybrid method GA-SA outperforms other state-of-the-art algorithms.

A transformative shift toward blockchain‐basedIoTenvironments: Consensus, smart contracts, and future directions

AbstractRecently, Internet‐of‐Things (IoT) based applications have shifted from centralized infrastructures to decentralized ecosystems, owing to user data's security and privacy limitations. The shift has opened new doors for intruders to launch distributed attacks in diverse IoT scenarios that jeopardize the application environments. Moreover, as heterogeneous and autonomous networks communicate, the attacks intensify, which justifies the requirement of trust as a key policy. Recently, blockchain‐based IoT solutions have been proposed that address trust limitations by maintaining data consistency, immutability, and chronology in IoT environments. However, IoT ecosystems are resource‐constrained and have low bandwidth and finite computing power of sensor nodes. Thus, the inclusion of blockchain requires an effective policy design regarding consensus and smart contract environments in heterogeneous IoT applications. Recent studies have presented blockchain as a potential solution in IoT, but an effective view of consensus and smart contract design to meet the end application requirements is an open problem. Motivated by the same, the survey presents the integration of suitable low‐powered consensus protocols and smart contract design to assess and validate the blockchain‐IoT ecosystems. We present blockchain‐IoT's emerging communication and security aspects with performance issues of consensus protocols, interoperability, and implementation platforms. A case study of a smart contract‐based blockchain‐driven ecosystem is presented with a comparative analysis of mining cost and latency, which shows its suitability in real‐world setups. We also highlight attacks on blockchain IoT, open issues, potential findings, and future directions. The survey intends to drive novel solutions for future consensus and safe, smart contract designs to support applicative IoT ecosystems.

AI-Empowered Fast Task Execution Decision for Delay-Sensitive IoT Applications in Edge Computing Networks

As the number of smart connected devices increases day by day, a massive amount of tasks are generated by various types of Internet of Things (IoT) devices. Intelligent edge computing is a promising enabler in next-generation wireless networks to execute these tasks on proximate edge servers instead of smart devices. Additionally, regarding the execution of tasks in edge servers, smart devices could provide a low-latency environment to the end users. Within this paper, an artificial intelligence (AI)-empowered fast task execution method in heterogeneous IoT applications is proposed to reduce decision latency by taking into account different system parameters such as the execution deadline of the task, battery level of devices, channel conditions between mobile devices and edge servers, and edge server capacity. In edge computing scenarios, the number of task requests, resource constraints of edge servers, mobility of connected devices, and energy consumption are the main performance considerations. In this paper, the AI-empowered fast task decision method is proposed to solve the multi-device edge computing task execution problem by formulating it as a multi-class classification problem. The extensive simulation results demonstrate that the proposed framework is extremely fast and precise in decision-making for offloading computation tasks compared to the conventional Lyapunov optimization-based algorithm results by ensuring the guaranteed quality of experience.

Customised Intrusion Detection for an Industrial IoT Heterogeneous Network Based on Machine Learning Algorithms Called FTL-CID

Technological breakthroughs in the Internet of Things (IoT) easily promote smart lives for humans by connecting everything through the Internet. The de facto standardised IoT routing strategy is the routing protocol for low-power and lossy networks (RPL), which is applied in various heterogeneous IoT applications. Hence, the increase in reliance on the IoT requires focus on the security of the RPL protocol. The top defence layer is an intrusion detection system (IDS), and the heterogeneous characteristics of the IoT and variety of novel intrusions make the design of the RPL IDS significantly complex. Most existing IDS solutions are unified models and cannot detect novel RPL intrusions. Therefore, the RPL requires a customised global attack knowledge-based IDS model to identify both existing and novel intrusions in order to enhance its security. Federated transfer learning (FTL) is a trending topic that paves the way to designing a customised RPL-IoT IDS security model in a heterogeneous IoT environment. In this paper, we propose a federated-transfer-learning-assisted customised distributed IDS (FT-CID) model to detect RPL intrusion in a heterogeneous IoT. The design process of FT-CID includes three steps: dataset collection, FTL-assisted edge IDS learning, and intrusion detection. Initially, the central server initialises the FT-CID with a predefined learning model and observes the unique features of different RPL-IoTs to construct a local model. The experimental model generates an RPL-IIoT dataset with normal and abnormal traffic through simulation on the Contiki-NG OS. Secondly, the edge IDSs are trained using the local parameters and the globally shared parameters generated by the central server through federation and aggregation of different local parameters of various edges. Hence, transfer learning is exploited to update the server's and edges' local and global parameters based on relational knowledge. It also builds and customised IDS model with partial retraining through local learning based on globally shared server knowledge. Finally, the customised IDS in the FT-CID model enforces the detection of intrusions in heterogeneous IoT networks. Moreover, the FT-CID model accomplishes high RPL security by implicitly utilising the local and global parameters of different IoTs with the assistance of FTL. The FT-CID detects RPL intrusions with an accuracy of 85.52% in tests on a heterogeneous IoT network.

Efficient data transmission using trusted third party in smart home environments

Currently, deployed Internet of Things (IoT) technology acts as a passive observer of the environment that sends data to a remote location. Developing and deploying future IoT applications will need re-tasking this one-way behaviour in a reliable manner. A novel computationally tractable optimization technique that can accept cross-layer resource configurations and focus on network enhancement with longevity should be created for the smart home, one of the heterogeneous IoT applications. This study shows different smart home architectures in static and mobile environments, taking into account some of the challenges like orchestration, mobility, and range in IoT. For network communication, routing protocol over 6LoWPAN (RPL) is used. The goal of the work is to optimize the communication network in both static and mobile environment. To attain the goal, this paper proposes an algorithm that improves the path selection by modifying the existing objective functions of RPL. The proposed smart home architectures are analysed and compared based on different parameters such as packet reception ratio, network overhead, throughput, average latency, and total energy consumption. Even when some of the devices in the smart home are mobile, the modified smart home-optimized path (MSHOP) is found to achieve a packet reception ratio of 99.93%, minimum latency of 0.9 s, and the highest total energy usage in the network of 3373 millijoules. In conclusion, proposed MSHOP outperforms all existing smart home architectures when considering network efficiency, time, and usability.

Toward QoS Prediction Based on Temporal Transformers for IoT Applications

Internet of Things (IoT) devices generate a tremendous amount of time series data that is extremely dynamic, heterogeneous and time dependent. Such types of data introduce significant challenges for the real-time prediction of QoS metrics of IoT applications with different traffic characteristics. To this end, in this paper, we propose a temporal transformer model and a unified system to predict several QoS metrics of heterogeneous IoT applications when they communicate with the Edge of the network. The transformer model also leverages an attention module to provide a solution for both short-term and long-term sequence prediction of QoS metrics that allows to better extract any time dependencies. In particular, in our framework, we firstly generate a set of datasets containing real-time traffic information of five different IoT applications such as Heating, Ventilation, and Air Conditioning (HVAC), lighting, Voice over Internet Protocol (VoIP), surveillance and emergency response using the 802.15.4 access technology and the RPL routing protocol. Following, we perform the data cleaning, downsampling and pre-processing of the datasets and we construct the QoS datasets, which include four QoS metrics, namely throughput, packet delivery ratio, packet loss ratio and latency. Finally, we evaluate the transformer model through extensive experimentation using both short-term and long-term dependencies and we show that our model can guarantee a robust performance and accurate QoS prediction.

Managing Heterogeneous and Time-Sensitive IoT Applications through Collaborative and Energy-Aware Resource Allocation

In the Internet of Things (IoT) environment, the computing resources available in the cloud are often unable to meet the latency constraints of time critical applications due to the large distance between the cloud and data sources (IoT devices). The adoption of edge computing can help the cloud deliver services that meet time critical application requirements. However, it is challenging to meet the IoT application demands while using the resources smartly to reduce energy consumption at the edge of the network. In this context, we propose a fully distributed resource allocation algorithm for the IoT-edge-cloud environment, which (i) increases the infrastructure resource usage by promoting the collaboration between edge nodes, (ii) supports the heterogeneity and generic requirements of applications, and (iii) reduces the application latency and increases the energy efficiency of the edge. We compare our algorithm with a non-collaborative vertical offloading and with a horizontal approach based on edge collaboration. Results of simulations showed that the proposed algorithm is able to reduce 49.95% of the IoT application request end-to-end latency, increase 95.35% of the edge node utilization, and enhance the energy efficiency in terms of the edge node power consumption by 92.63% in comparison to the best performances of vertical and collaboration approaches.

DeepEdge: A New QoE-Based Resource Allocation Framework Using Deep Reinforcement Learning for Future Heterogeneous Edge-IoT Applications

Edge computing is emerging to empower the future of Internet of Things (IoT) applications. However, due to heterogeneity of applications, it is a significant challenge for the edge cloud to effectively allocate multidimensional limited resources (CPU, memory, storage, bandwidth, etc.) with constraints of applications’ Quality of Service (QoS) requirements. In this paper, we address the resource allocation problem in Edge-IoT systems through developing a novel framework named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DeepEdge</i> that allocates resources to the heterogeneous IoT applications with the goal of maximizing users’ Quality of Experience (QoE). To achieve this goal, we develop a novel QoE model that considers aligning the heterogeneous requirements of IoT applications to the available edge resources. The alignment is achieved through selection of QoS requirement range that can be satisfied by the available resources. In addition, we propose a novel two-stage deep reinforcement learning (DRL) scheme that effectively allocates edge resources to serve the IoT applications and maximize the users’ QoE. Unlike the typical DRL, our scheme exploits deep neural networks (DNN) to improve actions’ exploration by using DNN to map the Edge-IoT state to joint resource allocation action that consists of resource allocation and QoS class. The joint action not only maximize users’ QoE and satisfies heterogeneous applications’ requirements but also align the QoS requirements to the available resources. In addition, we develop a Q-value approximation approach to tackle the large space problem of Edge-IoT. Further evaluation shows that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DeepEdge</i> brings considerable improvements in terms of QoE, latency and application tasks’ success ratio in comparison to the existing resource allocation schemes.

REERS: Reliable and energy‐efficient route selection algorithm for heterogeneous Internet of things applications

SummaryMachine‐type communication (MTC) such as device‐to‐device and machine‐to‐machine communication is a technology used to realize the concept of Internet of things (IoT). The fifth generation (5G) IoT is composed of large density of wireless sensor device. The massive roll out of the MTC devices is a serious challenge for wireless communication networks from operational and management perspective, including massive access, route selection, energy efficiency and network congestion. Many proposals have been put forward by the research community to cater to the issues and difficulties of massive MTC (mMTC) access in large density sensor network‐enabled IoT communication environment. Recently, data aggregation with efficient route selection has attracted a lot of research attention owing to its robust ability to resolve the above‐mentioned challenges. Existing data routing and aggregation methodologies are not efficient when adopted to heterogeneous IoT applications as they fail to bring trade‐off between energy efficiency and latency minimization. This research work proposes reliable and energy‐efficient route selection (REERS) scheme to reduce energy dissipation and latency for catering both real‐time and non‐real‐time applications. Experiment outcomes shows the REERS schemes achieves much superior performance than existing routing and aggregation methodologies over latency reduction and energy efficiency.

Energy-Efficient Multivariate Privacy-Aware RF Spectrum Reservation in Wireless Virtualization for Wireless Internet of Things

Next generation communication systems are expected to be heterogeneous and to have varying Quality of Service (QoS) requirements for different Internet of Things (IoT) applications. Wireless virtualization is regarded as an emerging technology capable of handling the ever growing resource demands by those heterogeneous IoT applications and systems by enabling wireless resource sharing through slicing. Scarce wireless resources and expensive physical infrastructure are leased to business units. The business units do not own the infrastructure, but rather gain access through a subleasing process. However, due to the lack of efficient management of wireless resources, energy efficiency of the physical infrastructure cannot be guaranteed. A viable solution to this problem is to anticipate resource needs and proactively implement energy efficient measures to ensure QoS requirements of users are met using a reservation scheme. Previous spectrum reservation approaches do not consider energy efficiency in the reservation process. In this paper, we present an adaptive resource reservation scheme for wireless network virtualization. The proposed scheme relies on an informed estimate of resource needs by a combination of aggregated event data from multiple contributors, and prediction based on previous allocation data. We present a Privacy aware Aggregation Model (PrivAgg) that relies on the truthfulness of contributors by providing secure enrollment and communication. We also present a prediction algorithm, Volume and Bandwidth-conditioned Spectrum Selective Moving Average (VBSSMA), that enforces a multivariate filter for the allocation history in order to increase the accuracy of prediction. We evaluate the performance of the reservation scheme and algorithms using theoretical analysis and numerical results from extensive simulations. Numerical results over multiple configurations for the weight of the aggregator and predictor components shows that VBSSMA results in up to 27% less allocation cost and 4% less error than existing Volume-conditioned Spectrum Selective Moving Average reservation approach.

A Cross‐Domain Authentication Optimization Scheme between Heterogeneous IoT Applications

With the continuous enrichment of the Internet of Things (IoT) applications, the demand for value exchange and collaborative control between heterogeneous IoT applications is increasing. However, the user management space varies depending on the IoT application, where the security domain stands as an example. It is one of the key technologies of data sharing between heterogeneous IoT organizations to cross the boundary of the security domain and verify the identity and authority of users in other security domains. Aiming at the slow speed of authentication protocol authority authentication during cross‐domain access and without considering the actual cross‐domain situation, the same cryptographic system parameters are used for all communication nodes in a cross‐domain environment. This article proposes a heterogeneous Internet of Things data access authority authentication scheme between applications. Based on certificate‐less public key cryptography and smart contract technology, a certificate‐less cross‐domain authentication scheme that supports parameter differentiation is designed and implemented. The theoretical and empirical analyses, comparing the communication volume, identity signature, and verification calculation cost, validated that the method proposed improves the cross‐domain identity authorization authentication ability and supports the use of differentiated cryptographic system parameters among different IoT applications.

Survey on Network Slicing for Internet of Things Realization in 5G Networks

Internet of Things (IoT) is an emerging technology that makes people's lives smart by conquering a plethora of diverse application and service areas. In near future, the fifth-generation (5G) wireless networks provide the connectivity for this IoT ecosystem. It has been carefully designed to facilitate the exponential growth in the IoT field. Network slicing is one of the key technologies in the 5G architecture that has the ability to divide the physical network into multiple logical networks (i.e., slices) with different network characteristics. Therefore, network slicing is also a key enabler of realisation of IoT in 5G. Network slicing can satisfy the various networking demands by heterogeneous IoT applications via dedicated slices. In this survey, we present a comprehensive analysis of the exploitation of network slicing in IoT realisation. We discuss network slicing utilisation in different IoT application scenarios, along with the technical challenges that can be solved via network slicing. Furthermore, integration challenges and open research problems related to the network slicing in the IoT realisation are also discussed in this paper. Finally, we discuss the role of other emerging technologies and concepts, such as blockchain and Artificial Intelligence/Machine Learning (AI/ML) in network slicing and IoT integration.

Systematic Approach for State-of-the-Art Architectures and System-on-Chip Selection for Heterogeneous IoT Applications

The Internet of Things (IoT) refers to a network of physical devices, which collects data and processes into a system without human intervention. In the commercialized market, IoT architectures are upgrading day by day to reduce data transmission costs, latency, and bandwidth usage for various application requirements. The extensively available IoT architectures and their specification resist the researchers to select a system-on-chip (SoC) for heterogeneous IoT applications. This paper seeks to comprehend the various IoT device specifications and their characteristics to support multiple applications. Moreover, microprocessor architectures and their components are detailed to facilitate developer knowledge in advanced methodology and technology. The various instructions set architectures (ISA) are implemented in a Zynq-7000 (xc7Zz20clg484-1) FPGA device to examine the feasibility of design space requirements for real-time hardware execution. To select specific system-on-chip (SoC) architecture for heterogeneous IoT applications, a genetic algorithm (GA) based optimization method is implemented in MATLAB. The proposed algorithm identifies the optimized SoC architecture concerning device parameters such as a clock, cache, RAM space, external storage, network support, etc. Further, the confusion matrix method evaluates the proposed algorithm's accuracy, which yields 84.62% accuracy. The outcome of SoCs attained through the GA are tested by analyzing their execution time and performance using various evaluation benchmarks. This article helps the researchers and field engineers to comprehend the microarchitecture device configurations and to identify the superior SoC for next-generation IoT practices.

Enhanced Online Q-Learning Scheme for Resource Allocation with Maximum Utility and Fairness in Edge-IoT Networks

Internet of Things (IoT) is experiencing an explosion in the data traffic due to the increase in the number of heterogeneous applications. The existing cloud computing models will not be capable to support the IoT applications that are delay-sensitive and using high bandwidth. The Edge-IoT systems represented by shared edge clouds support a wide range of IoT applications. Edge clouds provide resources closer to the IoT devices to tackle the delay sensitivity and bandwidth issues. However, the allocation of these resources with guaranteed application's utility in the context of Edge-IoT with multiple heterogeneous IoT applications, various resource demands, and limited resource availability is challenging. In this paper, we propose a novel enhanced online Q-learning scheme to allocate resources from edge clouds to IoT applications to maximize their utility and maintain allocation fairness among them. The developed online Q-learning scheme approximates its Q-value to tackle the problem of large state space, reduce the required learning computation, and expedite the system convergence. It is implemented using two settings: centralized using a dedicated controller at the edge cloud and distributed where edge servers learn cooperatively to achieve a common goal of finding joint resource allocation policy that maximizes the IoT applications' utilities. Extensive numerical results demonstrate the capability of the proposed scheme in improving applications' utilities and allocation fairness.

CE-IoT: Cost-Effective Cloud-Edge Resource Provisioning for Heterogeneous IoT Applications

With the great advance in the Internet-of-Things (IoT) sector, the recent years have witnessed an unprecedented wave of the proliferation of heterogeneous IoT devices and applications. Among them, some have stringent hard deadlines which can only be satisfied by the emerging paradigm of mobile-edge computing (MEC), while the others may pose elastic soft deadlines which can be flexibly fulfilled by cloud computing. However, with the presence of both temporal and spatial diversities of the resource cost of MEC and cloud, it remains a practical challenge how to efficiently provision the MEC and cloud resource to minimize the long-term operational cost, while still guaranteeing both hard and soft deadlines for heterogeneous IoT applications. To navigate such an inherent performance–cost tradeoff, an efficient online cloud-edge resource provisioning framework is proposed, based on the delay-aware Lyapunov optimization technique. Without requiring a priori knowledge of the statistics of the cloud-edge system, the proposed framework allows to make online greedy decisions on how much MEC and cloud resources to be provisioned to heterogeneous IoT applications. Through rigorous theoretical analysis, we prove that without violating both the hard and soft deadlines of heterogeneous IoT applications, the long-term operational cost can be pushed arbitrarily close to the offline optimum. With extensive evaluations driven by realistic traffic and cost traces, we empirically demonstrate the cost efficiency of the proposed cloud-edge resource provisioning framework.

Rate-Adaptive Fog Service Platform for Heterogeneous IoT Applications

With the advancement of the Internet of Things (IoT) technologies, the number of heterogeneous IoT applications requiring a variety of resources and services is increasing dramatically. Recently, the introduction of fog computing has further unlocked the potential of real-time services within the IoT context. On the basis of fog architecture, we herein propose a novel rate-adaptive fog service platform aiming at heterogeneous services provisioning and optimized service rate allocation. By forming several service groups in the fog network in which each service could be adequately provisioned, service consumers would always benefit from the fact that the majority of services produced by the IoT applications are in their proximity and thus are delivered to the destination promptly. Taking advantage of the well-known network utility maximization (NUM) approach, a service rate-adaptive algorithm is developed to empower fog nodes working together to adjust service delivery rate dynamically. Throughout this process, the algorithm takes the current network condition and constraint into account to ensure the rate is calibrated in favor of providing satisfactory quality of service (QoS) to each service receiver at the same time. Compared to other resource allocation strategies that mainly focus on allocating resources for a single network service, our proposed platform is capable of not only dealing with both the elastic and inelastic services but also handling the abrupt network changes and converging back to the global optimum rapidly.

An Investigation on Several Operating Systems for Internet of Things

In the field of development, Internet of things (IoT) plays a crucial role in providing solution to various situations. A lot of research has been conducted recently to model IoT based operating systems as standard UNIX, Windows and current real time operating systems are unable to meet the demand of heterogeneous IoT applications. In this paper we will focus on major OS features such as architecture, programming model, portability, memory management, real-time environment, scheduling algorithm, hardware support, networking and energy efficiency. We will be focusing on the following six operating systems which are as follows: Contiki, Tiny OS, RIOT, Zypher, Mbed and Brillo.

SAHCI: Scheduling Approach for Heterogeneous Content-Centric IoT Applications

The Internet of Things (IoT) is one of the dominating paradigms of the new era with its abilities to provide ubiquitous intelligence and pervasive interconnections to diverse physical objects. With the advancements, such as new generation 5G communication and cloud/edge computing-based paradigms, the degree of domination is expected to further increase. Therefore, improvements for quality of service have become a critical issue. Traditional packets scheduling algorithms cannot meet the requirements of the large-scale IoT systems. In this paper, a novel scheduling approach is proposed for different data classes (types) to be exchanged between heterogeneous nodes of a generic IoT infrastructure. The possible ways of organizing these classes depending on different metrics, such as time latency, reliability, and data loss, are investigated. The proposed approach has the advantage of being able to tune the priorities and network characteristics to reach a specifically desired performance state. Since each type of packets is considered separately, it is possible to prioritize them, by tuning the related parameters, which changes the priorities between packets. The numerical results presented show that the new approach performs better than the existing typical scheduling approaches. The developed approach can be used in the various IoT applications with the support of 5G communication and edge computing, such as agriculture, wearables, connected cars, smart retail, and smart cities.

Resource Allocation for Ultra-Reliable and Enhanced Mobile Broadband IoT Applications in Fog Network

In recent years, in order to provide a better quality of service (QoS) to Internet of Things (IoT) devices, the cloud computing paradigm has shifted toward the edge. However, the resource capacity (e.g., bandwidth) in fog network technology is limited and it is essential to efficiently bind the IoT applications with stringent QoS requirements with the available network infrastructure. In this paper, we formulate a joint user association and resource allocation problem in the downlink of the fog network, considering the evergrowing demand of QoS requirements imposed by the ultra-reliable low latency communications and enhanced mobile broadband services. First, we determine the priority of different QoS requirements of heterogeneous IoT applications at the fog network by enforcing the analytical framework using an analytic hierarchy process (AHP). Using the AHP, we then formulate a two-sided matching game to initiate stable association between the fog network infrastructure (i.e., fog devices) and IoT devices. Subsequently, we consider the externalities in the matching game that occurs due to job delay and solve the network resource allocation problem by applying the “best-fit” resource allocation strategy during matching. The simulation results illustrate the stability of the user association and efficiency of resource allocation with higher utility gain.

Multi-Access Mobile Edge Computing for Heterogeneous IoT

The articles in this special section focus on multi-access mobile edge computing (MA-MEC) for heterogeneous Internet of Things applications. These articles aim at soliciting high quality and unpublished work regarding recent advances in MA-MEC, with the main focus on addressing the fundamental design issues in MA-MEC, and the emerging paradigms and testbeds that use MA-MEC.The convergence of mobile Internet and wireless systems has witnessed an explosive growth in resource-hungry and computation-intensive services and applications, which cover a wide range of so-called heterogeneous Internet of Things (H-IoTs) systems including real-time video/audio surveillance, remote e-health systems, intelligent transportation systems, Internet of Vehicles, etc. Mobile edge computing, by placing various cloud resources (e.g., computational and storage resources) closer to smart devices/objects, has been envisioned as an enabling and highly promising technology to realize and reap the promising benefi ts of H-IoTs applications.