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The rapid growth of the Internet-of-things (IoT) in recent years has led to the development of new access technologies targeting low-power wide area networks (LPWANs). LPWAN is a promising solution for long-range and low-power IoT and machine-to-machine (M2M) communication applications. However, practical LPWAN deployments suffer from intense collisions due to uncoordinated transmissions, given the dense deployment of devices and wide coverage area. Therefore, this paper proposes a deep learning-based collision aware transmission priority scheduling technique (CA-PST) to mitigate high packet collision rate in ultra-dense wireless networks. The proposed CA-PST is applied to the low-range wide area network (LoRaWAN) platform where the LoRa gateway configures the nodes to a particular transmission protocol classes based on the predicted number of packet collisions. Aided by unsupervised learning clustering, the nodes located at higher transmission priority clusters will be allowed to communicate with the gateway using class C and therefore avoid packet collisions. Whereas nodes in lower transmission priority clusters communicate with the gateway using class A. Therefore, the CA-PST offers a fair trade-off between the packet delivery rate (PDR) and total energy consumption. Extensive simulation results show that using CA-PST can significantly improve the LPWAN reliability in terms of PDR in ultra-dense networks.

Intelligent transport systems (ITS) are pivotal in the development of sustainable and green urban living. ITS is data-driven and enabled by the profusion of sensors ranging from pneumatic tubes to smart cameras which are used to detect and categorise passing vehicles. Simple sensors, such as pneumatic tubes, are successfully deployed for counting passing vehicles but are not useful for vehicle tracking or re-identification. Smart cameras, on the other hand, collect comprehensive information but suffer from occlusion, patchy coverage, and compromised vision in adverse weather and visibility. This work explores a novel ITS data source based on an optical fibre which acts as an uninterrupted length of virtual sensors using a distributed acoustic sensor (DAS) system. Based on real DAS data collected in the field, we first present a study of latent DAS features that uniquely identify a given vehicle, otherwise referred to as the vehicle signature. We formulate a classification problem that examines incoming DAS data to extract vehicle signatures and identify the different types of vehicle. To this end, we implement different classification methods and present a comparative performance analysis that reveals novel insights into the potential role of DAS for ITS applications. This work is a pilot study of DAS for vehicle classification that is driven by real DAS data and validated by promising results where a vehicle’s type is correctly identified with 94% accuracy and the size of a vehicle with 95% accuracy.

An intelligent reflecting surface (IRS) is proposed to enhance the physical layer security in the Rician fading channel wherein the angular direction of the eavesdropper (ED) is aligned with a legitimate user. A two-phase communication system under active attacks and passive eavesdropping is considered in this scenario. The base station avoids direct transmission to the attacked user in the first phase, whereas other users cooperate in forwarding signals to the attacked user in the second phase, with the help of IRS and energy harvesting technology. Under the occurrence of active attacks, an outage-constrained beamforming design problem is investigated under the statistical cascaded channel error model, which is solved by using the Bernstein-type inequality. An average secrecy rate maximization problem for the passive eavesdropping is formulated, which is then addressed by a low-complexity algorithm. The numerical results of this study reveal that the negative effect of the ED’s channel error is larger than that of the legitimate user.

With the tremendous demand for connectivity anywhere and anytime, existing network architectures should be modified. To cope with the challenges that arise due to the increasing flood of devices/users and a diverse range of application requirements, new technologies and concepts must be integrated to enable their benefits. Service providers and business companies are looking for new areas of research to enhance overall system performance. This article gives a detailed survey about the recent 5G technologies, the solutions they provide, and the effect caused by their addition to current cellular networks. It is based on the three most important 5G concepts: Device to Device (D2D), Network Slicing (NS), and Mobile Edge Computing (MEC). This study proposes to design the future 5G networks by the integration of all three technologies. It is believed that spectrum efficiency, energy efficiency, and overall throughput will be greatly improved by using D2D. The system delay and computational load will be reduced as tasks will be handled by edge routers located at the base stations. Thus offloading the core network and the system capital expenses and operational expenses will be reduced significantly by slicing the network.

Fifth Generation (5G) cellular networks aim to overcome the pressing demands posed by dynamic Quality of Service (QoS) constraints, which have primarily remained unaddressed using conventional network infrastructure. Cellular networks of the future necessitate the formulation of efficient resource allocation schemes that readily meet throughput requirements. The idea of combining Device-to-Device (D2D), Mobile Edge Computing (MEC), and Network slicing (NS) can improve spectrum utilization with better performance and scalability. This work presents a spectrum efficiency optimization problem in D2D based 5G-Heterogeneous Cellular Network (5G-HCN) with NS. Owing to the shortage of resources, we propose an underlay model where macro-cell users (MUs), small-cell users (SUs), and D2D users (DUs) reuse the resources while considering the effects of interference. The goal is to maximize the average network spectrum efficiency (SE) and throughput without degrading the system performance. The problem at hand is naturally a non-convex mixed-integer non-linear programming (MINLP) problem that is intractable. Therefore, we have suggested a distributed resource allocation strategy with an edge computing (DRA-EC) approach to find the sub-optimal solution. In distributed augmented Lagrange method, each edge router located at BS will solve its problem locally, and the consensus algorithm will find the global solution using these local estimates. The central slice controller will cut the customized network slices according to the bandwidth requirements of each user type with optimized spectrum information. The simulation outcomes prove that our proposed method is near the central optimization scheme with low computational complexity. It is much better because it reduces the computational time and system overhead.

Long-Range (LoRa) communication technology is considered as a promising connectivity solutions for Internet of Things (IoT) dense applications. In particular, LoRa has drawn the interest due to its low power consumption and wide area coverage. Despite the benefits of LoRaWAN protocol, it still suffers from excessive random and simultaneous transmissions due to the adoption of ALOHA protocol. Therefore, resulting in severe packet collision rate as the network scales up. This leads to continuous retransmission attempts, which in return increase the transmission delay and energy consumption. Thus, this paper proposes a dynamic transmission Priority Scheduling Technique (PST) based on the unsupervised learning clustering algorithm to reduce the packet collision rate and enhance the network's transmission delay and energy consumption. Particularly, the LoRa gateway classifies the nodes into different transmission priority clusters. While the dynamic PST allows the gateway to configure the transmission intervals for the nodes according to the transmission priorities of the corresponding clusters. This work allows scaling up the network density while maintaining low packet collision rate and significantly enhances the transmission delay & the energy consumption. Simulation results show that the proposed work outperforms the typical LoRaWAN and recent clustering & scheduling schemes. Therefore, the proposed work is well suited for dense applications in LoRaWAN.

This paper proposes an energy and carbon footprint modelling using artificial intelligence technique to assess the impact of occupant density for various types of office building. We use EnergyPlus to simulate energy consumption, and then estimate the related CO₂ emissions based on three years (2016–2018) of Actual Meteorological Year (AMY) weather data. Various occupant densities were used to evaluate the annual energy consumption and CO₂ emission. In this work, a robust deep learning technique of long short-term memory (LSTM) model was established to predict the time-series energy consumption and CO₂ emissions. A power exponential curve was suggested to correlate the behaviour of annual energy and CO₂ emission for occupant densities range from 10 to 100 m²/person for each office building type. The results of LSTM model show high prediction performance and small variations within the three types of office building data, which can be applied to the similar building model to predict and optimise energy consumption and CO₂ emission.

A semi-outsourcing privacy-preserving scheme is proposed in this paper for the IoT data collection named semi-outsourcing privacy-preserving (SOPP), which supports delegated identity authentication for the IoT devices without revealing the transmitted data. Compared with other schemes that implement the authentication based upon using trusted cloud services, the design of our scheme SOPP can achieve the delegated authentication on untrusted public clouds while providing privacy-preserving data transmission. Meanwhile, the implemented one-way authentication can reduce the communication cost for the IoT devices (especially for the low-resource ones) to prolong their battery life. The performance of the SOPP scheme is demonstrated for its use in the resource-constrained IoT devices and compared with a benchmark trusted cloud scheme including one based upon certificates and an interactive (two-way) authentication scheme.

The electric vehicle (EV) charging scheme can reduce the power generation costs and improve the smart grid resilience. However, the huge penetrations of EVs can impact the voltage stability and operating costs. In this paper, a novel EV participation charging scheme is proposed for a decentralized blockchain-enabled smart grid system. Our objectives are to minimize the power fluctuation level in the grid network and the overall charging cost for EV users. We first formulate the power fluctuation level problem of the smart grid system that take into accounts of EV battery capacities, charging rates, and EV users charging behavior. And then, we propose a novel adaptive blockchain-based electric vehicle participation (AdBEV) scheme that uses the Iceberg order execution algorithm to obtain an improved EV charging and discharging schedule. The simulation results show the proposed scheme outperforms the scheme that applying genetic algorithm approach in term of lowering the power fluctuation level and overall charging costs.

In the above article [1], reference [2] was missing. The corrections are made in Section VI of [1]. Figures 5 and 7 of [1] are also cited.

Permissioned blockchains can be applied for sharing data among permitted users to authorise the data access requests in a permissioned blockchain. A consensus network constructed using pre-selected nodes should verify a data requester’s credentials to determine if he or she have the correct permissions to access the queried data. However, current studies do not consider how to protect users’ privacy for data authorisation if the pre-selected nodes become untrusted, e.g., the pre-selected nodes are manipulated by attackers. When a user’s credentials are exposed to pre-selected nodes in the consensus network during authorisation, the untrusted (or even malicious) pre-selected nodes may collect a user’s credentials and other private information without the user’s right to know. Therefore, the private data exposed to the consensus network should be tightly restricted. In this paper, we propose a challenge-response based authorisation scheme for permissioned blockchain networks named Challenge-Response Assisted Access Authorisation (CRA3) to protect users’ credentials during authorisation. In CRA3, the pre-selected nodes in the consensus network do not require users’ credentials to authorise data access requests to prevent privacy leakage when these nodes are compromised or manipulated by attackers. Furthermore, the computational burden on the consensus network for authorisation is reduced because the major computing work of the authorisation is executed by the data requester and provider in CRA3.

The traditional grid operation is unfortunately lacking the resilience and responsiveness in reacting to contingency events due to the poor utilization of available resources in mitigating the shortfalls. Such an unaddressed issue may affect the grid stability and the ultimate grid blackout. Therefore, this paper models a grid optimization module consisting of a mid and low (microgrid) voltage level grid component of an urban grid network for a disaster recovery. The model minimizes the cost of generation required to meet the demand through the economic dispatch in combination with the unit commitment. Two optimization problems are formulated that resemble the grid operation: normal (grid-connected) and islanded. A constrained-based linear programming optimization problem is used to solve the formulated problems, where the dual-simplex algorithm is used as the linear solver. The model ensures sufficient demand to be met during the outages through the N-1 contingency criterion for critical infrastructures. The simulation length is limited to 24 h and is solved using the MATLAB® R2017b software. Three different cases are established to evaluated the modelled grid resilience during the grid-connected or the islanding of operations subject to adversed events. The simulated results provide the economical outage recovery that will maintain the grid resilience across the grid.

Routing Protocol for Low power and Lossy network (RPL) topology attacks can downgrade the network performance significantly by disrupting the optimal protocol structure. To detect such threats, we propose a RPL-specification, obtained by a semi-auto profiling technique that constructs a high-level abstract of operations through network simulation traces, to use as reference for verifying the node behaviors. This specification, including all the legitimate protocol states and transitions with corresponding statistics, will be implemented as a set of rules in the intrusion detection agents, in the form of the cluster heads propagated to monitor the whole network. In order to save resources, we set the cluster members to report related information about itself and other neighbors to the cluster head instead of making the head overhearing all the communication. As a result, information about a cluster member will be reported by different neighbors, which allow the cluster head to do cross-check. We propose to record the sequence in RPL Information Object (DIO) and Information Solicitation (DIS) messages to eliminate the synchronized issue created by the delay in transmitting the report, in which the cluster head only does cross-check on information that come from sources with the same sequence. Simulation results show that the proposed Intrusion Detection System (IDS) has a high accuracy rate in detecting RPL topology attacks, while only creating insignificant overhead (about 6.3%) that enable its scalability in large-scale network.