Mission-critical Internet Of Things Applications Research Articles

Statistical Learning-Based Grant-Free Access for Delay-Sensitive Internet of Things Applications

Mission-critical Internet-of-Things (IoT) applications require communication interfaces that provide ultra-reliability and low latency. Acquiring knowledge regarding the number of active devices and their latency-reliability requirements becomes essential to optimize resource allocation in heterogeneous networks. Due to the inherent heavy computation overheads, the conventional centralized decision-making approaches result in large latency. The distributed computing and device-level prediction of network parameters can play a significant role in designing mission-critical IoT applications operating in dynamic environments. This paper considers the medium access control (MAC) layer of heterogeneous networks employing a framed-ALOHA-based restricted transmission strategy to enhance reliability. We present a statistical learning-based device-level network exploration mechanism in which end-devices use their transmission history to predict different network parameters. The IoT devices share the learned parameters with the base station (BS) to identify different groups presented in the network. The simulation results show that the mean square error (MSE) in predicting different network parameters can be reduced by increasing the history window size. In this regard, the optimal size of the history window under the given accuracy constraints is also determined. We demonstrate that the proposed device-level network load prediction mechanism is more robust as compared to the BS-centered approach.

QoE Based Revenue Maximizing Dynamic Resource Allocation and Pricing for Fog-Enabled Mission-Critical IoT Applications

Fog computing is becoming a vital component for Internet of things (IoT) applications, acting as its computational engine. Mission-critical IoT applications are highly sensitive to latency, which depends on the physical location of the cloud server. Fog nodes of varying response rates are available to the cloud service provider (CSP) and it is faced with a challenge of forwarding the sequentially received IoT data to one of the fog nodes for processing. Since the arrival times and nature of requests is random, it is important to optimally classify the requests in real-time and allocate available virtual machine instances (VMIs) at the fog nodes to provide a high QoE to the users and consequently generate higher revenues for the CSP. In this paper, we use a pricing policy based on the QoE of the applications as a result of the allocation and obtain an optimal dynamic allocation rule based on the statistical information of the computational requests. The developed solution is statistically optimal, dynamic, and implementable in real-time as opposed to other static matching schemes in the literature. The performance of the proposed framework has been evaluated using simulations and the results show significant improvement as compared with benchmark schemes.

A Novel Trust-Aware and Energy-Aware Clustering Method That Uses Stochastic Fractal Search in IoT-Enabled Wireless Sensor Networks

Wireless sensor network (WSN) technology is considered to be an integral part of large-scale and efficient deployment of Internet-of-Things (IoT). More specifically, in mission-critical IoT applications, trust in the sensor data is becoming increasingly important. Sensor nodes have limited processing, storage, and communication capabilities, which make them susceptible to attacks and unreliable functioning. However, the limitations in the energy resources of the sensors are a major challenge in maximizing the network’s lifetime. Grouping the sensors into clusters was proposed to address such energy limitations. Many meta-heuristic clustering protocols have been proposed to maximize the network lifetime, which is an NP-hard problem. This problem is more complicated when considering the trust factor. The majority of existing clustering models were built to reduce the energy consumption in the network without considering the energy consumption required to detect untrusted nodes, and thus, it requires extra energy consumption to perform this task. This article proposes a clustering protocol with a trust model that detects the untrusted nodes through energy and data-trust. In addition, the proposed clustering protocol maximizes the network’s lifetime through the good characteristics of stochastic fractal search optimization. Finally, a novel fitness function is introduced to select the cluster-heads among the trusted nodes. The function is based on the following four parameters: 1) the remaining energy of the nodes; 2) the density of the nodes; 3) the distance between each node and the base-station; and 4) the network’s dissipated energy. When forming the clusters, the density of the cluster-heads is considered to balance the load of all of the cluster-heads. The experimental evaluation performed here affirms the efficacy of the proposed protocol in comparison with existing protocols.

Reliable Real-Time Operating System for IoT Devices

Internet of Things (IoT) technologies have so deeply penetrated our daily lives that IoT devices have been everywhere. Not only do IoT devices provide basic means to convenient living but they also offer intelligent services tailored to diverse user needs. For instance, beyond remotely turning ON and OFF small gadgets, such as lamps and switches, users can now control appliances via smartphones and also interact with them verbally. In the backdrop of multifarious, complex IoT applications, their reliability becomes of paramount importance. This article focuses on achieving user-level reliability in an IoT operating system (OS) that executes both real-time (RT) and non-real-time (NRT) tasks concurrently. For example, smart appliances typically run RT tasks such as a motor controller, as well as NRT tasks such as Wi-Fi-based network functions, concurrently. In general, RT tasks are critical and should be protected from error-prone tasks. Otherwise, a motor controller failure may cause appliances to malfunction. To this end, we propose a novel, reliable IoT OS, called TizenRT that achieves user-level reliability through two key features, namely, fault isolation and fast recovery , while maintaining RT constraints. Our evaluation shows that TizenRT can isolate faulty tasks in memory space, while guaranteeing normal RT tasks to complete their missions within a required time, e.g., 50 $\mu \text{s}$ . Moreover, corrupted tasks can be recovered within a few milliseconds, e.g., 10 ms, without the need for a system reboot. This makes TizenRT a preferred choice for mission-critical IoT applications.

Secure Short-Packet Communications for Mission-Critical IoT Applications

In pervasive Internet of Things (IoT) applications, the use of short packets is expected to meet the stringent latency requirement in ultra-reliable low-latency communications; however, the incurred security issues and the impact of finite blocklength coding on the physical-layer security have not been well understood. This paper comprehensively investigates the performance of secure short-packet communications in a mission-critical IoT system with an external multi-antenna eavesdropper. An analytical framework is proposed to approximate the average achievable secrecy throughput of the system with finite blocklength coding. To gain more insight, a simple case with a single-antenna access point (AP) is considered first, in which the secrecy throughput is approximated in a closed form. Based on that result, the optimal blocklengths to maximize the secrecy throughput with and without the reliability and latency constraints, respectively, are derived. For the case with a multi-antenna AP, following the proposed analytical framework, closed-form approximations for the secrecy throughput are obtained under both beamforming and artificial-noise-aided transmission schemes. Numerical results verify the accuracy of the proposed approximations and illustrate the impact of the system parameters on the tradeoff between transmission latency and reliability under the secrecy constraint.

Downlink MIMO-NOMA for Ultra-Reliable Low-Latency Communications

With the emergence of the mission-critical Internet of Things applications, ultra-reliable low-latency communications are attracting a lot of attentions. Non-orthogonal multiple access (NOMA) with multiple-input multiple-output (MIMO) is one of the promising candidates to enhance connectivity, reliability, and latency performance of the emerging applications. In this paper, we derive a closed-form upper bound for the delay target violation probability in the downlink MIMO-NOMA, by applying stochastic network calculus to the Mellin transforms of service processes. A key contribution is that we prove that the infinite-length Mellin transforms resulting from the non-negligible interferences of NOMA are Cauchy convergent and can be asymptotically approached by a finite truncated binomial series in the closed form. By exploiting the asymptotically accurate truncated binomial series, another important contribution is that we identify the critical condition for the optimal power allocation of MIMO-NOMA to achieve consistent latency and reliability between the receivers. The condition is employed to minimize the total transmit power, given a latency and reliability requirement of the receivers. It is also used to prove that the minimal total transmit power needs to change linearly with the path losses, to maintain latency and reliability at the receivers. This enables the power allocation for mobile MIMO-NOMA receivers to be effectively tracked. The extensive simulations corroborate the accuracy and effectiveness of the proposed model and the identified critical condition.

5G NB-IoT: Efficient Network Traffic Filtering for Multitenant IoT Cellular Networks

Internet of Things (IoT) is a key business driver for the upcoming fifth-generation (5G) mobile networks, which in turn will enable numerous innovative IoT applications such as smart city, mobile health, and other massive IoT use cases being defined in 5G standards. To truly unlock the hidden value of such mission-critical IoT applications in a large scale in the 5G era, advanced self-protection capabilities are entailed in 5G-based Narrowband IoT (NB-IoT) networks to efficiently fight off cyber-attacks such as widespread Distributed Denial of Service (DDoS) attacks. However, insufficient research has been conducted in this crucial area, in particular, few if any solutions are capable of dealing with the multiple encapsulated 5G traffic for IoT security management. This paper proposes and prototypes a new security framework to achieve the highly desirable self-organizing networking capabilities to secure virtualized, multitenant 5G-based IoT traffic through an autonomic control loop featured with efficient 5G-aware traffic filtering. Empirical results have validated the design and implementation and demonstrated the efficiency of the proposed system, which is capable of processing thousands of 5G-aware traffic filtering rules and thus enables timely protection against large-scale attacks.