The edge computing paradigm enables mobile devices with limited memory and processing power to execute delay-sensitive, compute-intensive, and bandwidth-intensive applications on the network by bringing the computational power and storage capacity closer to end users. Edge computing comprises heterogeneous computing platforms with resource constraints that are geographically distributed all over the network. As users are mobile and applications change over time, identifying an optimal task scheduling method is a complex multi-objective optimization problem that is NP-hard, meaning the exhaustive search with a time complexity that grows exponentially can solve the problem. Therefore, various approaches are utilized to discover a good solution for scheduling the tasks within a reasonable time complexity, while achieving the most optimal solution takes exponential time. This study reviews task scheduling algorithms based on centralized and distributed methods in a three-layer computing architecture to identify their strengths and limitations in scheduling tasks to edge service nodes.

The field of the Internet of Things (IoT) is growing at a breakneck pace and its applications are becoming increasingly sophisticated with time. Fault injection attacks on IoT systems are aimed at altering software behavior by introducing faults into the hardware devices of the system. Attackers introduce glitches into hardware components, such as the clock generator, microcontroller, and voltage source, which can affect software functioning, causing it to misbehave. The methods proposed in the literature to handle fault injection attacks on IoT systems vary from hardware-based attack detection using system-level properties to analyzing the IoT software for vulnerabilities against fault injection attacks. This paper provides a systematic review of the various techniques proposed in the literature to counter fault injection attacks at both the system level and the software level to identify their limitations and propose solutions to address them. Hybrid attack detection methods at the software level are proposed to enhance the security of IoT systems against fault injection attacks. Solutions to the identified limitations are suggested using machine learning, dynamic code instrumentation tools, hardware emulation platforms, and concepts from the software testing domain. Future research possibilities, such as the use of software fault injection tools and supervised machine learning for attack detection at the software level, are investigated.

Smart policing refers to the use of advanced technologies such as artificial intelligence to enhance policing activities in terms of crime prevention or crime reduction. Artificial intelligence tools, including machine learning and natural language processing, have widespread applications across various fields, such as healthcare, business, and law enforcement. By means of these technologies, smart policing enables organizations to efficiently process and analyze large volumes of data. Some examples of smart policing applications are fingerprint detection, DNA matching, CCTV surveillance, and crime prediction. While artificial intelligence offers the potential to reduce human errors and biases, it is still essential to acknowledge that the algorithms reflect the data on which they are trained, which are inherently collected by human inputs. Considering the critical role of the police in ensuring public safety, the adoption of these algorithms demands careful and thoughtful implementation. This paper presents a systematic literature review focused on exploring the machine learning techniques employed by law enforcement agencies. It aims to shed light on the benefits and limitations of utilizing these techniques in smart policing and provide insights into the effectiveness and challenges associated with the integration of machine learning in law enforcement practices.

Fog computing is a promising option for time sensitive vehicular over-the-air (OTA) updates, as it can offer enhanced network durability and lower communication delays, versus the cloud. Fog node utilization is non-deterministic, largely owing to vehicular traffic patterns. The resultant over provisioning of resources manifests itself in increased communication and handover delays. Based on an analysis of regional traffic patterns, our proposed algorithm determines the optimal number of fog nodes required for OTA updates. The efficacy of our proposed approach is demonstrated using a case study that considers handover delay, propagation delay, transmission rate and vehicular mobility to predict the OTA update time. We employ a machine learning model for predicting the communication delay between fog devices and vehicles. Using European WiFi hotspot signal strength NYC dataset and 5G dataset, we observe that the proposed approach increases net reserve fog resources by 26.57% on average, and reduces the OTA update time by 5.34%. We test the scalability of our approach by analyzing the performance in terms of average throughput while varying the number of vehicles and OTA update size. The performance of the proposed OTA update scheme on simulations has been corroborated by implementation on a real-world testbed.

As embedded systems become more prominent in society, the technologies that run on them must be used efficiently. One such technology is the Neural Network (NN). NN’s, combined with the Internet of Things (IoT), can utilize the massive amounts of data produced to optimize, control, and automate embedded systems, giving them more functionality than ever before. However, the status quo of offloading all NN functionality onto external devices has many flaws. It forces the embedded system to entirely rely on networks that may have high latency or connection issues. Networks may also expose them to security risks. To reduce the reliance of IoT devices on networks, we examined several solutions, such as delegating some NN’s to run solely on the IoT device or splitting the NN and distributing the subnetworks into different devices. It was found that, for shallow NN’s, the IoT device itself could run the NN at a rate faster than offloading it to an external device, but the IoT device needed to offload its inputs once the NN’s started to increase in layers and complexity. When splitting the NN, it was found that the number of messages sent between devices could be reduced by up to 97% while only reducing the accuracy of the NN by 3%.

Whether in the realm of software or hardware, datasets representing the state of systems are mostly imbalanced. This imbalance is because these systems’ reliability requirements make the occurrence of an anomaly a rare phenomenon. Hence, most datasets on anomaly detection have a relatively small percentage that captures the anomaly. Recently, generative adversarial networks (GAN) have shown promising results in image generation tasks. Therefore, in this research work, we build on conditional GANs (CGAN) to generate plausible distributions of a given profile to solve the challenge of data imbalance in anomaly detection tasks and present a novel framework for anomaly detection. Firstly, we learn the pattern of the minority class data samples using a single class CGAN. Secondly, we use the knowledge base of the single class CGAN to generate samples that augment the minority class samples so that a binary class CGAN can train on the typical and malicious profiles with a balanced dataset. This approach inherently eliminates the bias imposed on algorithms from the dataset and results in a robust framework with improved generalization. Thirdly, the binary class CGAN generates a knowledge base that we use to construct the cluster-based anomaly detector. During testing, we do not use the single class CGAN, thereby providing us with a lean and efficient algorithm for anomaly detection that can do anomaly detection on semisupervised and non-parametric multivariate data. We test the framework on logs and image-based anomaly detection datasets with class imbalance.We compare the performance of AD-CGAN with GAN-derived and non-GAN-derived state of the art algorithms on benchmark datasets. AD-CGAN outperforms most of the algorithms in the standard metrics of Precision, Recall, and F-1 Score. Where AD-CGAN does not perform better in the parameters used, it has the advantage of being lightweight. Therefore, it can be deployed for both online and offline anomaly detection tasks since it does not use an input sample inversion strategy.