In this article, we consider an unmanned aerial vehicle (UAV) with an in-band full-duplex radio that is used to interconnect industrial Internet of things (IIoT) devices and exploit their computation and energy resources to help process data. We formulate a mixed integer linear program to optimize the first sampling rate of each device, second amount of data the UAV transmits and receives to/from a device, third position of the UAV over time, and finally the number of virtual machines used by devices and UAV to compute data. We also propose a distributed protocol to determine quantity using aforementioned points. Our results show that an IBFD-UAV has a higher max–min computed sampling rate as compared to when the UAV uses a half duplex radio. Moreover, the said protocol achieves a max–min rate that is 80% optimal.

This article considers the problem of monitoring one or more mobile targets over a given planning horizon. Unlike previous works, it leverages ambient backscatter communication to reduce the energy expenditure of sensor nodes in order to prolong coverage lifetime. We outline a mixed integer linear program (MILP) that aims to maximize the number of time slots in which all mobile targets are monitored by a sensor node; a.k.a. complete mobile targets coverage. For a given targets trajectory, it outputs the activation time of sensor nodes to ensure all targets are monitored by at least one sensor node. Further, we propose an algorithm called maximum energy opportunity selection (MEOS), which uses the energy level of sensor nodes to set their operation mode. The simulation results show that the complete mobile targets coverage lifetime of MILP and MEOS algorithms improves when nodes employ backscatter communications. Specifically, it leads to a 79% improvement in complete mobile targets coverage lifetime.

In this article, we study an industrial Internet of Things (IIoT) network with a sink/gateway that is capable of decoding multiple transmissions via successive interference cancellation (SIC), and energy harvesting devices tasked with providing complete targets coverage of targets. In particular, we study a novel question: how to schedule the sensing and transmission of these devices to ensure complete target coverage over time? We outline a mixed integer linear program (MILP) and two heuristic solutions that jointly optimize the active time and transmit power of devices. Our simulation results show that the complete targets coverage lifetime of our heuristic solutions is within 80% of the optimal complete targets coverage lifetime, as computed by MILP.

Many applications, e.g., digital twins, rely on sensing data from Internet of Things (IoT) networks, which is used to infer event(s) and initiate actions to affect an environment. This gives rise to concerns relating to data integrity and provenance. One possible solution to address these concerns is to employ blockchain. However, blockchain has high resource requirements, thereby making it unsuitable for use on resource-constrained IoT devices. To this end, this paper proposes a novel approach, called two-layer directed acyclic graph (2LDAG), whereby IoT devices only store a digital fingerprint of data generated by their neighbors. Further, it proposes a novel proof-of-path (PoP) protocol that allows an operator or digital twin to verify data in an on-demand manner. The simulation results show 2LDAG has storage and communication cost that is respectively two and three orders of magnitude lower than traditional blockchain and also blockchains that use a DAG structure. Moreover, 2LDAG achieves consensus even when 49% of nodes are malicious.

Blockchain is the foundation of emerging applications, such as smart contracts, nonfungible token (NFT), and metaverse. A key issue is that blockchain requires massive storage space, which limits its deployment in resource-limited end devices, e.g., Internet of Things. Recently, coded blockchain is proposed to reduce the storage requirement of blockchain while guaranteeing its security and data integrity. Coded blockchain encodes blocks into coded symbols, which are then distributively stored by clients. A key challenge when applying coded blockchain in resource-restricted networks is to ensure all clients store the same, and also the minimum, number of coded blocks. To this end, this article addresses a novel problem that minimizes the maximum (min–max) storage requirement of clients. It formulates the said problem as an integer linear program (ILP). It then proposes centralized algorithms to improve the computational efficiency of storage assignments. Moreover, it presents distributed algorithms that satisfy the distributive property of blockchain. Numerical results show that the proposed distributed algorithm with a short length code reduces the min–max storage of clients by 80% compared with traditional blockchain. In addition, the computational complexity of distributed algorithms is significantly lower than centralized algorithms.

Secure file storage and distribution is a key challenge in the information era. It is important to ensure the files are not tampered or eavesdropped during storage and transmission. A promising solution is to store the files in blockchain. However, the massive blockchain data makes it hard to operate a file blockchain in resource restricted end devices. To this end, this paper proposes a File Storage in Coded Blockchain (FSC) solution that leverage the advantage of error correction code. In particular, the block data can be encoded and distributed stored at different nodes to reduce the storage requirement. In addition, this paper propose a Coding Practical Byzantine Fault Tolerant (C-PBFT) consensus protocol to distribute the coded information with reduced communication overhead. Simulation results show that the proposed FSC significantly reduces the storage requirement as compared with traditional blockchain technologies.

Recently, many works seek to exploit the negligible transmission cost of backscattering radio frequency (RF) signals, and also demonstrated its feasibility in allowing passive or batteryless tags to communicate over multiple hops. In this context, this article studies data collection in amultihopInternet of Things (IoT) wireless network consisting of tags equipped with sensor(s). These tags forward data via tag-to-tag communications to a gateway. Our aim is for the gateway to collect the maximum amount of data from tags over a given time frame. To do so, we optimize the time used by tags to sample their environment and data transmission, which involves solving an NP-hard link scheduling problem. We present a mixed integer nonlinear program (MINLP) to determine the transmitting tags in each time slot as well as the sensing duration of tags. We also propose a heuristic, called Max-L, that aims to maximize the number of links in each transmission set in order to reduce the transmission time of samples. Our results show that Max-L collects 85% of the optimal amount of samples.

This article studies a novel problem that aims to maximize the number of uploaded samples by devices in wireless-powered Internet of Things (IoT) networks. To do so, it takes advantage of ambient backscatter communications (AmBC) to help sensor devices conserve energy, and thus leaving them with more energy to collect samples. We outline a mixed-integer linear program (MILP) that aims to determine the operation mode of each device in each time slot in order to maximize the total amount of uploaded samples. We also present a heuristic approach to set the operation mode of devices based on their residual energy and data. Our results show that as compared to the case without AmBC, the total data uploaded by devices increases by 48% and 45% for the MILP and heuristic, respectively-both of which exploit AmBC.

In the future, sensor nodes or Internet of Things (IoTs) will be tasked with sampling the environment. These nodes/devices are likely to be powered by a Hybrid Access Point (HAP) wirelessly, and may be programmed by the HAP with a sampling time to collect sensory data, carry out computation, and transmit sensed data to the HAP. A key challenge, however, is random channel gains, which cause sensor nodes to receive varying amounts of Radio Frequency (RF) energy. To this end, we formulate a stochastic program to determine the charging time of the HAP and sampling time of sensor nodes. Our objective is to minimize the expected penalty incurred when sensor nodes experience an energy shortfall. We consider two cases: single and multi time slots. In the former, we determine a suitable HAP charging time and nodes sampling time on a slot-by-slot basis whilst the latter considers the best charging and sampling time for use in the next T slots. We conduct experiments over channel gains drawn from the Gaussian, Rayleigh or Rician distribution. Numerical results confirm our stochastic program can be used to compute good charging and sampling times that incur the minimum penalty over the said distributions.