Mobile IoT Networks Research Articles

Intelligent Resource Management at the Edge for Ubiquitous IoT: An SDN-Based Federated Learning Approach

The ubiquitous nature of Internet of Things (IoT) devices has posited many challenges that need innovative solutions in the 5G era. Software defined networks (SDNs) are becoming indispensable in managing several aspects of next-generation IoT networking that arise from the need to control highly heterogeneous, geographically dispersed, mobile IoT devices. One such aspect is cache management at the edge. Recently, multiple forms of edge resources, including mobile device clouds and micro-edge data centers have emerged to provide scalable cache placement locations that reduce the costs for the mobile network operator (MNO). As all of these service locations are registered with the MNO (or links established after registration with the 5G base station, BS), content should be placed according to the user's demand and the cost the user is willing to pay to receive the desired level of QoS. To this end, it is important to understand the future popularity of the content for its optimal placement considering the highly dynamic user mobility. In this article, we address two key aspects of a mobile IoT network: security and seamless connectivity for data delivery. We rely on the federated learning (FL) architecture, which enables harnessing data and computational capabilities at end-user devices to train machine learning models. We study FL concepts in the domain of edge computing for IoT use cases, such as caching. We draw conclusions from various state-of-the-art models and posit several challenges that can be overcome via a novel proposed control algorithm.

Elman-Based Secure Data Transmission Quality Prediction for Complex IoT Networks

The information age has brought earth-shaking changes. For interconnection of all things, the data transmission has widely employed the Internet of Things (IoT). The IoT transmission faces complex environments. The secure data transmission is very important for mobile IoT networks. The secure data transmission quality prediction is investigated for mobile IoT networks. The probability of strictly positive secrecy capacity (SPSC) is used to evaluate the secure data transmission quality, and the expressions are first derived. Then, employing Elman network, a secure data transmission quality intelligent prediction approach is proposed. The extensive simulations are run to evaluate the proposed approach. The simulation results show that the Elman-based approach can achieve a higher quality precision than other methods. The Elman-based approach also can achieve a lower time complexity.

Mobility Support 5G Architecture with Real-Time Routing for Sustainable Smart Cities

The Internet of Things (IoT) is an emerging technology and provides connectivity among physical objects with the support of 5G communication. In recent decades, there have been a lot of applications based on IoT technology for the sustainability of smart cities, such as farming, e-healthcare, education, smart homes, weather monitoring, etc. These applications communicate in a collaborative manner between embedded IoT devices and systematize daily routine tasks. In the literature, many solutions facilitate remote users to gather the observed data by accessing the stored information on the cloud network and lead to smart systems. However, most of the solutions raise significant research challenges regarding information sharing in mobile IoT networks and must be able to stabilize the performance of smart operations in terms of security and intelligence. Many solutions are based on 5G communication to support high user mobility and increase the connectivity among a huge number of IoT devices. However, such approaches lack user and data privacy against anonymous threats and incur resource costs. In this paper, we present a mobility support 5G architecture with real-time routing for sustainable smart cities that aims to decrease the loss of data against network disconnectivity and increase the reliability for 5G-based public healthcare networks. The proposed architecture firstly establishes a mutual relationship among the nodes and mobile sink with shared secret information and lightweight processing. Secondly, multi-secured levels are proposed to protect the interaction with smart transmission systems by increasing the trust threshold over the insecure channels. The conducted experiments are analyzed, and it is concluded that their performance significantly increases the information sustainability for mobile networks in terms of security and routing.

Current Research Trends in IoT Security: A Systematic Mapping Study

The smart mobile Internet-of-things (IoT) network lays the foundation of the fourth industrial revolution, the era of hyperconnectivity, hyperintelligence, and hyperconvergence. As this revolution gains momentum, the security of smart mobile IoT networks becomes an essential research topic. This study aimed to provide comprehensive insights on IoT security. To this end, we conducted a systematic mapping study of the literature to identify evolving trends in IoT security and determine research subjects. We reviewed the literature from January 2009 to August 2020 to identify influential researchers and trends of keywords. We additionally performed structural topic modeling to identify current research topics and the most promising ones via topic trend estimation. We synthesized and interpreted the results of the systematic mapping study to devise future research directions. The results obtained from this study are useful to understand current trends in IoT security and provide insights into research and development of IoT security.

Performance Analysis and Prediction for Mobile Internet-of-Things (IoT) Networks: A CNN Approach

With the increasingly mature sensor technology and the increasing popularity of broadband network, “the Internet-of-Everything” era is coming, and the mobile Internet of Things (IoT) is booming around the world. However, the mobile IoT communication networks face serious challenges, which are caused by the complex and variable communication environments. The mobile IoT applications can produce large-scale data, which will consume substantial energy. The transmit antenna selection (TAS) and cooperative communication schemes are commonly used to reduce the complexity and the energy consumption, which directly impact the performance of mobile IoT networks. To evaluate the performance of mobile IoT networks, it is important to analyze outage probability (OP) performance. In this article, we investigate the OP performance analysis of mobile IoT communication networks and propose an OP intelligent prediction algorithm based on an improved convolutional neural network (CNN). First, the mobile OP performance is analyzed by combining the TAS and decode-and-forward cooperative schemes, and the exact OP expressions are derived. Then, an improved CNN is designed to avoid the loss of important information, which contains the input layer, three-convolution layer, one fully connected layer, and output layer. The proposed CNN-based prediction approach is compared with the radial basis function (RBF), generalized regression (GR), Elman, and extreme learning machine (ELM) methods. The simulation results validate that the proposed CNN prediction approach can achieve a better prediction effect than RBF, Elman, GR, and ELM methods. For the CNN approach, it has a 44% increase in the prediction accuracy.

A formal consensus-based distributed monitoring approach for mobile IoT networks

Internet of Things (IoT) represent a significant area of network research due to the many opportunities derived from the problematics and applications. The most recurring problematics are the mobility, the availability and also the limited resources. A well-known interest in networks and therefore in IoT is to monitor properties of the network and nodes [1, 2]. The problematics can have a significant impact on the monitoring efforts. Mobility and availability can create incomplete results for the monitoring. It can also represent a challenge to monitor distributed properties. The literature states that accuracy is not always reliable and difficult to achieve due to dynamic properties of the IoT in particular with M2M communications and mobile devices. Therefore we propose a distributed monitoring architecture that relies on multiple points of observation. It provides a consensus mechanism that allows it to aggregate and provides a more meaningful and accurate result. We support our proposal with numerous mathematical definitions that model local results for a single node and global results for the network. Finally, we evaluate our architecture with an emulator that relies on AWS, NS3, and Docker with varying number of nodes, network size, network density, speed, mobility algorithms and timeouts. We obtain very promising results, especially regarding accuracy.

Intelligent Outage Probability Prediction for Mobile IoT Networks Based on an IGWO-Elman Neural Network

Advances in sensor technology have accelerated the development of mobile internet applications and contributed to the tremendous growth of the Internet of Things (IoT). Mobile IoT networks typically operate in complex and highly variable urban wireless channel environments. This makes effective and reliable communications challenging. To ensure efficient and stable communications, mobile IoT networks must adapt to the complex environment. This can be achieved by predicting the outage probability (OP). This paper investigates OP analysis and prediction for these networks. Exact closed-form OP expressions are derived and Monte-Carlo simulation is used to verify the analysis and evaluate the OP performance. Then, an OP prediction approach based on an improved grey wolf optimization (IGWO) algorithm and Elman neural network (IGWO-Elman) is proposed. The IGWO algorithm employs improved opposition-based learning to optimize the population initialization and ensure sufficient population diversity. The Elman neural network uses the IGWO algorithm to obtain good network parameters. Simulation results are presented which demonstrate that the prediction accuracy of IGWO-Elman is better than SVM, ELM, and BP algorithms. In terms of prediction accuracy, the IGWO-Elman algorithm is increased by 44%.

Distributed learning dynamics of Multi-Armed Bandits for edge intelligence

Abstract Multi-agent decision making is a fundamental problem in edge intelligence. In this paper, we study this problem for IoT networks under the distributed Multi-Armed Bandits (MAB) model. Most of existing works for distributed MAB demand long-time stable networks connected by powerful devices and hence may not be suitable for mobile IoT networks with harsh IoT constraints. To meet the challenge of resource constraints in mobile IoT environment, we propose a lightweight and robust learning algorithm in a dynamic network allowing topology changes. In our model, each agent is assumed to have only limited memory and communicate with each other asynchronously. Moreover, we assume that the bandwidth for exchanging information is limited and each agent can transmit O ( log 2 K ) bits ( K denotes the number of arms) per communication. Rigorous analysis shows that despite these harsh constraints, the best arm/option can be identified collaboratively by the agents and the algorithm converges efficiently. Extensive experiments illustrate that the proposed algorithm exhibits good efficiency and stability in mobile settings.

Efficient and Secure Multi-User Multi-Task Computation Offloading for Mobile-Edge Computing in Mobile IoT Networks

Mobile edge computing (MEC) is a new paradigm to alleviate resource limitations of mobile IoT networks through computation offloading with low latency. This article presents an efficient and secure multi-user multi-task computation offloading model with guaranteed performance in latency, energy, and security for mobile-edge computing. It does not only investigate offloading strategy but also considers resource allocation, compression and security issues. Firstly, to guarantee efficient utilization of the shared resource in multi-user scenarios, radio and computation resources are jointly addressed. In addition, JPEG and MPEG4 compression algorithms are used to reduce the transfer overhead. To fulfill security requirements, a security layer is introduced to protect the transmitted data from cyber-attacks. Furthermore, an integrated model of resource allocation, compression, and security is formulated as an integer nonlinear problem with the objective of minimizing the weighted sum of energy under a latency constraint. As this problem is considered as NP-hard, linearization and relaxation approaches are applied to transform the problem into a convex one. Finally, an efficient offloading algorithm is designed with detailed processes to make the computation offloading decision for computation tasks of mobile users. Simulation results show that our model not only saves about 46% of system overhead consumption in comparison with local execution but also scale well for large-scale IoT networks.

Ant Colony System for Energy Consumption Optimization in Mobile IoT Networks

In this paper, a new algorithm is proposed for computing the node-disjoint optimal transmission energy consumption route for coded cooperative mobile IoT networks. Inspired by the potential benefits user cooperation can provide, we incorporate user cooperation to the mobile ad-hoc multi-hop IoT networks. Our results include a novel ant colony system-based node-disjoint energy-efficient routing algorithm. Ant colony system can approximate the optimal route by local information and is thus very suitable for mobile IoT network environment. In particular, ant algorithm makes history-sensitive choice and thus can significantly outperform the greedy algorithm. In addition, it can efficiently handle the case of multiple sources and multiple destinations. For a large IoT network, we investigate the multi-scale ant colony system and when compared to Dijkstra algorithm, such an algorithm usually shortens the runtime by a factor of several hundred.

Task Allocation in Fog-Aided Mobile IoT by Lyapunov Online Reinforcement Learning

Fog-aided mobile IoT is proposed to speed up service response by deploying fog nodes at network edges. We investigate the task allocation in fog-aided mobile IoT networks, where mobile users generate computing tasks at different locations and offload them to fog nodes, i.e., to intelligently distribute tasks to different fog nodes in order to adapt to the varying wireless channel conditions and different fog resources. The objective is to minimize the average task completion time constrained by the mobile device’s battery capacity and each task’s completion deadline. In practice, future tasks are usually unknown in advance owing to the unpredictable environments and hence an online algorithm is required to make decisions on the fly. Moreover, the local task information may be incomplete and hence historical statistics should be utilized to estimate the most appropriate fog node for the current task. Therefore, we design an online reinforcement learning algorithm to address the two challenges. We also derive and analyze the computational complexity and theoretical bound. Simulation results show that our online algorithm achieves the optimal performance asymptotically, illustrate the performances of our online reinforcement learning algorithm as compared with existing works, and validate the theoretical bound analysis.

Clustering and reliability-driven mitigation of routing attacks in massive IoT systems

As an integral component of the 5G communications, the massive Internet of Things (IoT) are vulnerable to various routing attacks due to their dynamic infrastructure, distinct computing resources, and heterogeneity of mobile objects. The sinkhole and selective forwarding attacks stand out among the most destructive ones for infrastructureless networks. Despite the countermeasures introduced by legacy intrusion detection systems (IDS), the massive IoT seeks novel solutions to address their unique requirements. This paper introduces DeTection of SinkHole And SelecTive ForwArding for Supporting SeCure routing for Internet of THIngs (THATACHI), a new IDS against sinkhole and selective forwarding attacks that target routing mechanism in massive and mobile IoT networks. To cope with the density and mobility challenges in the detection of attackers and ensuring reliability, THATACHI exploits watchdog, reputation and trust strategies. Our performance evaluation under an urban scenario shows that THATACHI can perform with a 99% detection rate, 6% of false negative and false positive rates. Moreover, when compared to its closest predecessor against sinkhole attacks for IoT, THATACHI runs with at least 50% less energy consumption.

Interplay between Big Spectrum Data and Mobile Internet of Things: Current solutions and future challenges

In the current epoch of technology trends, Internet of Things (IoT) is an emerging technology that illustrates the significant transition for enterprises to extend their businesses. Such technologies not only spark the growth in business, but also add values to our daily lives in many aspects such as livelihood, health, work, and entertainment. Nowadays, with the emergence of wireless infrastructure and the advancements in cellular technologies, Mobile IoT (MIoT) has gained considerable amount of attention. Intelligent decisions are made based on the information retrieved from raw data through sensors, machines, and other devices. These decisions are widely utilized in many fields of life such as smart health-care, smart transportation, and smart energy etc. Usually such smart systems are realized through MIoT infrastructure. The devices participating in MIoT are heterogeneous and produce huge amount of data, and contribute to cross-platform solutions covering plethora of applications. In the wake of such volume and variety, the data produced because of spectrum analysis for the entire IoT network is also huge and thus referred to as Big Spectrum Data (BSD). To this end, BSD is a way forward to get deep insights to the big data generated by MIoT. In this paper, we conduct a systematic review of the MIoT network focusing on the characteristics of mobility in MIoT from BSD perspective. More precisely, we focus on the relationship between MIoT and BSD, and discuss in detail, the BSD analytics to harness its benefits in MIoT. Furthermore, we investigate and outline the current solutions of BSD in MIoT. Additionally, through this work, we identify the existing challenges faced in dealing with BSD in MIoT and future research directions.

Improving the Performance of LOADng Routing Protocol in Mobile IoT Scenarios

Routing protocols represent an important issue on the Internet of Things (IoT) scenarios since they are responsible for creating paths and forwarding data packets among the network nodes. In mobile IoT scenarios, the topology changes caused by the movement of nodes makes the work of routing protocols more difficult. Thus, the current IoT routing solutions tend to present strong limitations and a poor performance in these scenarios, generally requiring complex additional improvement to better support the mobility of the devices. In this context, the Lightweight On-demand Ad hoc Distance-vector Routing Protocol - Next Generation (LOADng), is an emerging solution for IoT networks that despite being adequate for a mobile environment due to its reactive functioning still lacks in performance. Thus, this paper proposes a novel solution to enhance the performance of LOADng in mobile IoT networks. The improved version, LOADng-IoT-Mob, introduces a mechanism that permits nodes to be aware of the availability of their neighbors through the harnessing of control messages. As a result, these nodes can shorten paths and avoid sending data packets through broken routes due to the movement of the nodes. Additionally, a short periodical control message is introduced, allowing the nodes to update their routing table, even with a low control message frequency. Furthermore, a new routing metric is proposed for creating routes based on the reliability of the link and proximity of the neighboring nodes. Finally, through computational simulations, the performance of the LOADng-IoT-Mob is studied under multiple scenarios varying the network size, the number of mobile devices, and maximum nodes' speed. The results obtained demonstrate the efficiency of the proposed solution in terms of packet delivery ratio, latency, and power and overhead efficiency, with a slight increase in memory consumption.

How to Cache in Mobile Hybrid IoT Networks?

Content-centric mobile hybrid Internet-of-Things (IoT) networks consisting of mobile devices and static femto access points (FAPs) are studied, where each device moves according to the random walk mobility model and requests a content object from the library independently at random according to a Zipf popularity distribution. Instead of allowing access to content objects at macro base stations via costly backhaul providing connection to the core network, we consider a more practical scenario where mobile devices and static FAPs, each having a finite-size cache space, are able to cache a subset of content objects so that each request is served by other mobile devices or static FAPs. Under a general multihop-based content delivery protocol, we analyze the order-optimal throughput--delay trade-off by presenting a new cache allocation strategy. In particular, under a given caching strategy, we first characterize a throughput--delay trade-off in terms of scaling laws along with the general content delivery multihop routing protocol. Then, the order-optimal throughput--delay trade-off is characterized by presenting the order-optimal cache allocation strategy, which jointly finds the replication sets at mobile devices and static FAPs via a novel variable decoupling approach. In our mobile IoT network, an interesting observation is that highly popular content objects are mainly served by mobile devices while the rest of content objects are served by static FAPs. We perform numerical evaluation to validate our analytical results. We also show that the order-optimal strategy strictly outperforms a baseline approach, where the replication sets at mobile devices and static FAPs are optimized separately.

Lightweight NFC Protocol for Privacy Protection in Mobile IoT

The Internet of Things (IoT) aims to achieve the interconnection of all devices in our lives. Due to the complex network environment, the IoT with mobile devices often faces many security problems, such as privacy leakages and identity forgery attacks. As a developing technology in mobile IoT, near field communication (NFC) is widely used in electronic payments and identity authentications. The current NFC studies mainly focus on payment technology, but there are a few studies on privacy protection and the lightweight requirements in the mobile IoT authentication protocol. We focus on the lightweight privacy protection authentication technology in mobile IoT. In the paper, we summarize the clustering model in mobile IoT networks and propose a lightweight authentication protocol. A security analysis shows that the protocol can resist many security threats, such as privacy leakages, identity forgeries, and replay attacks. The simulation also shows that the protocol is lightweight, with the utilization of look-up-tables (LUTs) and registers in our protocol being less than 0.5%. Our work can provide a secure and lightweight mobile authentication serve in the NFC-based mobile IoT network such as smart home and office attendance.

Preallocated Duplicate Name Prefix Detection Mechanism Using Naming Pool in CCN Based Mobile IoT Networks

As the number of mobile devices and IoT (internet of things) devices has explosively increased, various contents are created anytime and anywhere. To meet such trend, current internet architecture has exposed many limitations such as high control overhead due to multistaged address resolution, frequent location updates, and network congestion. Recently, information centric networking (ICN) is considered as new networking architecture to redesign current internet’s content exchange paradigm. In current ICN architecture, whenever a mobile node moves to a new domain, it needs long latency to configure and confirm the temporary name prefix. So, this paper presents an efficient name prefix configuration mechanism in mobile CCN to reduce the latency needed for the name prefix configuration during MCS’s handover. From the performance analysis, the proposed mechanism is shown to provide lower control overhead and lower resource consumption.