Industrial Internet-of-Things (IIoT) technology has been rigorously developed in recent years, moving towards the ambitious goal of industry 4.0, Network Automation. However, there are a few critical challenges regarding the implementation of a reliable IIoT ecosystem for different applications; security, battery life, and bandwidth are controversial challenges. All the challenges regarding IIoT are mainly struggling within the edge computing smaller box of the big picture in which a cloud is an upstream object while sensors and actuators act as downstream devices. Therefore, having a reliable IIoT ecosystem necessitates focusing on the whole IIoT’s challenges in the edge computing smaller box; first realizing the vital, practical demands of a specific IIoT application, and then defining the compatible protocols to form the effective edge computing configuration. This paper reviews four IIoT case study applications with their specific requirements and their counterpart sensor/actuator properties to find the appropriate edge computing protocols satisfying their demands.

LoRa Wide Area Network (LoRaWAN) is one the of the most popular Internet of Things (IoT) technologies for long-range and low-cost communication. At present, LoRaWAN has been applied in a variety of applications, including localization, smart metering, etc. However, the increasing number of LoRaWAN devices would degrade their quality of service (QoS). There are two main reasons. The first reason is the competition of bandwidth and channel resources between large number of connected end devices. Another one is the redundant channel resources allocation configurations of most end devices to achieve better transmission reliability. To address these challenges, this work proposes an enhanced resource allocation scheme based on both k-means and k-prototype classification algorithms to mitigate the affection of ALOHA scheme and the multi-gateway interference problem. In this proposed scheme, intense network resources under dense end device scenario and the redundant claim of network resources configuration in end devices are considered. An outlier improved spreading factor distribution method is also proposed to reduce the negative effect of the problems. By evaluating and comparing packet loss rate and the relative distribution of spreading factors, an average of 22% increment in transmission performance of LoRaWAN networks is achieved.

LoRa wide area network (LoRaWAN), an emerging IoT protocol, has been popularized in large-scale applications, given its long-range and low-power properties. Hitherto, there is no appropriate traffic model for LoRaWAN to estimate the heterogeneous arriving traffic at the network server cluster (NSC). Inefficient computation power planning or even processing failure might be further caused. Radio replication, commonly existed in the arriving traffic at NSC in LoRaWAN, also causes difficulty estimating the makespan (i.e., mean processing time in NSC). To overcome the abovementioned limitations, a heterogeneous radio-replication-aware traffic aggregation model is proposed to estimate the arriving traffic for LoRaWAN. In addition, a radio-replication-combined supermarket model (RRC-SM), on top of HTAM, is proposed to achieve load balancing among servers in LoRaWAN. Furthermore, a nondominated sorting genetic algorithm based on multiobjective optimization is developed to simultaneously minimize cost and latency on NSC. Experiments reveal that the proposed HTAM and RRC-SM agree well with the simulation outcome. Under the arriving traffic estimated as 6.16 erlangs with four radio replications of each arriving packet on average, the proposed RRC-SM provides more than 50% reduction on the total processing latency and 75% reduction on the number of servers in NSC than other existing models.

Internet of Things (IoT) has become one of the most popular technologies in recent years, covering from citywide services to industrial applications, which enlarges the smart life for human beings. Through IoT, billions of IoT end devices can be interconnected to support various applications. The emergence of low-power wide-area network (LPWAN) technologies provides a great opportunity to support such an enormous network with their kilometer-level coverage and uA-level power consumption. To improve the efficiency of network resources of LPWANs, the cooperated IoT is proposed by researchers. However, the current LPWAN consists of diverse protocols, equipment, and design standards, rendering the increasing development effort on designing a compliance network by developers. To address this issue, the IEEE 1451, developed by Instrumentation and Measurement Society, is proposed. The IEEE 1451 standardized the wireless IoT systems with wireless transducer interface module (WTIM), network capable application processor server (NCAP Server) and NCAP Client. Besides, the application programming interfaces (APIs) and transducer electronic data sheet (TEDS) are also standardized. Based on the IEEE 1451, a standardized structure for LPWANs, namely IEEE1451-LPWAN is introduced. In addition, an M/M/1/N based queueing model is built to analyze the queueing performance of IEEE1451-LPWAN, which provides guidance for adopters in the future.

The received signal strength (RSS) finger-print-based approaches are widely used for indoor location-based services (LBSs). The emerging long range wide area network (LoRaWAN) is a cost-effective solution for indoor latency-tolerant LBSs attributed to its long-range property. In general, there are serious RSS fluctuations due to fadings along the communication path, thus significantly jeopardizing the localization accuracy. To overcome the challenge, in this article we propose the extreme RSS (ERSS) to stabilize the fingerprint database and formulate boundary autocorrelation to downsize tremendously the searching complexity and thus proliferating localization accuracy. In essence, the RSS fluctuations are modeled as a Bernoulli random process so that the RSS stability can be estimated by a newly defined fluctuation analytic function. To mitigate the impact of the perturbative fluctuation, the ERSS is further defined to cultivate a highly stable and robust fingerprint database which withstands environmental dynamics. In addition, boundary autocorrelation is developed to measure and compare the similarity between the measured RSS values versus the prestored fingerprint database. RSS values with low autocorrelation coefficients are eradicated from the typically lengthy searching. The downsized complexity significantly improves the localization accuracy. Experiments were carried out and the results revealed that the proposed method achieved sub-10-m localization accuracy in indoor environments. Such accuracy is encouraging and superior in contemporary LoRaWAN measurements.

In recent years, electric vehicles (EVs) have been widely applied to improve environment. The EV could provide environmentally friendly transportation but have the demerit of low battery capacity. Rapid charging by charging stations (CS) is critically needed especially for those drivers in long distance trip. Thus, a routing optimization problem for EVs charging should be addressed. Furthermore, this problem becomes more practical when the EV density is high at peak. In this scenario EVs are only allowed to obtain energy that render them able to arrive at the destination. In this paper, we formulate an emergent EV charging optimization problem in EV high density area which has not been discussed in related work and a novel genetic algorithm based emergent charging scheduling (GECS) scheme is proposed. The genetic algorithm (GA) is presented to simplify the multi-objectives optimization process in this case. Furthermore, incorporation of the Earliest Deadline First (EDF) which indicates the minimum recharging deadline time as the subject and Nearest Job First (NJF) which indicates the minimum recharging path as the subject into genetic optimization process can relieve the charging emergent condition and improve optimized results. The simulation results show that the proposed scheme can provide an optimal solution to minimize the average distance and waiting time for emergent charging in EV high density region.