Machine learning has traditionally been solely performed on servers and high-performance machines. However, advances in chip technology have given us miniature libraries that fit in our pockets and mobile processors have vastly increased in capability narrowing the vast gap between the simple processors embedded in such things and their more complex cousins in personal computers. Thus, with the current advancement in these devices, in terms of processing power, energy storage and memory capacity, the opportunity has arisen to extract great value in having on-device machine learning for Internet of Things (IoT) devices. Implementing machine learning inference on edge devices has huge potential and is still in its early stages. However, it is already more powerful than most realise. In this paper, a step forward has been taken to understand the feasibility of running machine learning algorithms, both training and inference, on a Raspberry Pi, an embedded version of the Android operating system designed for IoT device development. Three different algorithms: Random Forests, Support Vector Machine (SVM) and Multi-Layer Perceptron, respectively, have been tested using ten diverse data sets on the Raspberry Pi to profile their performance in terms of speed (training and inference), accuracy, and power consumption. As a result of the conducted tests, the SVM algorithm proved to be slightly faster in inference and more efficient in power consumption, but the Random Forest algorithm exhibited the highest accuracy. In addition to the performance results, we will discuss their usability scenarios and the idea of implementing more complex and taxing algorithms such as Deep Learning on these small devices in more details.

Mobile Ad Hoc Networks (MANET) are self-configuring infrastructureless networks of mobile devices connected via wireless links. Each device can send and receive data, but it should also forward traffic unrelated to its own use. All need to maintain their autonomy, and effectively preserve their resources (e.g. battery power). Moreover, they can leave the network at any time. Their intrinsic dynamicity and fault tolerance makes them suitable for applications, such as emergency response and disaster relief, when infrastructure is nonexistent or damaged due to natural disasters, such as earthquakes and flooding, as well as more mundane, day-today , uses where their flexibility would be advantageous. Routing is the fundamental research issue for such networks and refers to finding and maintaining routes between nodes. Moreover, it involves selecting the best route where many may be available. However, due to the freedom of movement of nodes, new routes need to be constantly recalculated. Most routing protocols use pure broadcasting to discover new routes, which takes up a substantial amount of bandwidth. Intelligent rebroadcasting reduces these overheads by calculating the usefulness of a rebroadcast, and the likelihood of message collisions. Unfortunately, this introduces latency and parts of the network may become unreachable. This paper discusses the Zone based Routing with Parallel Collision Guided Broadcasting Protocol (ZCG) that uses parallel and distributed broadcasting technique [8] to reduce redundant broadcasting and to accelerate the path discovery process, while maintaining a high reachability ratio as well as keeping node energy consumption low. ZCG uses a one hop clustering algorithm that splits the network into zones led by reliable leaders that are mostly static and have plentiful battery resources. The performance characteristics of the ZCG protocol are established through simulations by comparing it to other well-known routing protocols, namely the: AODV and DSR. It emerges that ZCG performs well under many circumstances.

The Internet of Things (IoT) is the result of the convergence of sensing, computing, and networking technologies, allowing devices of varying sizes and computational capabilities (things) to intercommunicate. This communication can be achieved locally enabling what is known as edge and fog computing, or through the well‐established Internet infrastructure, exploiting the computational resources in the cloud. The IoT paradigm enables a new breed of applications in various areas including health care, energy management and smart cities. This paper starts off with reviewing these applications and their potential benefits. Challenges facing the realization of such applications are then discussed. The sheer amount of data stemmed from devices forming the IoT requires new data mining systems and techniques that are discussed and categorized later in this paper. Finally, the paper is concluded with future research directions. This article is categorized under: Fundamental Concepts of Data and Knowledge > Big Data Mining Application Areas > Health Care Application Areas > Industry Specific Applications

The concept of molecular similarity has been commonly used in rational drug design, where structurally similar molecules are examined in molecular databases to retrieve functionally similar molecules. The most used conventional similarity methods used two-dimensional (2D) fingerprints to evaluate the similarity of molecules towards a target query. However, these descriptors include redundant and irrelevant features that might impact the performance of similarity searching methods. Thus, this study proposed a new approach for identifying the important features of molecules in chemical datasets based on the representation of the molecular features using Autoencoder (AE), with the aim of removing irrelevant and redundant features. The proposed approach experimented using the MDL Data Drug Report standard dataset (MDDR). Based on experimental findings, the proposed approach performed better than several existing benchmark similarity methods such as Tanimoto Similarity Method (TAN), Adapted Similarity Measure of Text Processing (ASMTP), and Quantum-Based Similarity Method (SQB). The results demonstrated that the performance achieved by the proposed approach has proven to be superior, particularly with the use of structurally heterogeneous datasets, where it yielded improved results compared to other previously used methods with the similar goal of improving molecular similarity searching.

Several agent-based and probabilistic models were proposed to simulate human behaviour, which is an important cause of high energy consumption in buildings. However, some of these models ignore behavioural energy waste at occupant level, and when they model it, they are based on small case studies and produce high level energy consumption data. This paper proposes a hybrid approach that integrates agent-based and probabilistic models to simulate behavioural energy waste at occupant level. The combination of the two approaches helps produce fine-grained data, and is based on large real data samples. The developed model was validated against realistic data. The results show that employment type have an effect on the energy consumption of households, which needs further investigation to quantify the effect and test other social parameters.