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Many industries now rely on drones to monitor infrastructures. In this respect, this article considers maximizing the revenue of an Industrial Internet of Things operator that provides two services: 1) data trading; and 2) drones rental. In service 1), the operator sells data of locations/points it acquired via drones. For service 2), it rents idle drones to users. The problem at hand is to determine the allocation of drones to services 1) and 2) that maximizes the operator's revenue over a given planning horizon. We outline a novel integer linear program (ILP) to solve the said problem, which can be used to determine the optimal number of drones assigned to both services. The ILP, however, requires an exhaustive collection of drone trajectories. We therefore present two heuristics called weighted-based algorithm (WBA) and genetic algorithm (GA) to generate trajectories for data collection. The results show that WBA earns 95.6% of the optimal revenue. GA is able to achieve 99% of ....
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A problem with machine learning (ML) techniques for detecting intrusions in the Internet of Things (IoT) is that they are ineffective in the detection of low-frequency intrusions. In addition, as ML models are trained using specific attack categories, they cannot recognize unknown attacks. This article integrates strategies of cost-sensitive learning and multitask learning into a hybrid ML model to address these two challenges. The hybrid model consists of an autoencoder for feature extraction and a support vector machine (SVM) for detecting intrusions. In the cost-sensitive learning phase for the class imbalance problem, the hinge loss layer is enhanced to make a classifier strong against low-distributed intrusions. Moreover, to detect unknown attacks, we formulate the SVM as a multitask problem. Experiments on the UNSW-NB15 and BoT-IoT datasets demonstrate the superiority of our model in terms of recall, precision, and F1-score averagely 92.2%, 96.2%, and 94.3%, respectively, over ot ....
In future Internet of Thing (IoT) networks, devices can be leveraged to compute tasks or services. To this end, this paper addresses a novel problem that requires devices to collaboratively execute tasks with dependencies. A key consideration is that in order to conserve energy, devices may execute a task in approximate mode, which generate errors. To optimize their operation mode, we outline a novel chance constrained program that aims to execute as many tasks as possible in approximate mode subject to a probabilistic constraint relating to the said errors. We also outline two novel solutions to determine task execution modes: (i) a sample average approximation (SAA) method, and (ii) a heuristic solution called MinC. We have studied the performance of SAA and MinC with Round Robin, which assigns tasks to devices in a round-robin manner. Specifically, we find that the maximum energy consumption of devices when using MinC and Round Robin is respectively around 14.2% and 23.1% higher tha ....