Wireless sensor network (WSN) systems are typically composed of thousands of sensors that are powered by limited energy resources. To extend the networks longevity, clustering techniques have been introduced to enhance energy efficiency. This paper presents a survey on clustering over the last two decades. Existing protocols are analyzed from a quality of service (QoS) perspective including three common objectives, those of energy efficiency, reliable communication and latency awareness. This review reveals that QoS aware clustering demands more attention. Furthermore, there is a need to clarify how to improve quality of user experience (QoE) through clustering. Understanding the users' requirements is critical in intelligent systems for the purpose of enabling the ability of supporting diverse scenarios. User awareness or user oriented design is one remaining challenging problem in clustering. In additional, this paper discusses the potential challenges of implementing clustering schemes to Internet of Things (IoT) systems in 5G networks. We indicate that clustering techniques enhanced with smart network selection solutions could highly benefit the QoS and QoE in IoT. As the current studies for WSNs are conducted either in homogeneous or low level heterogeneous networks, they are not ideal or even not able to function in highly dynamic IoT systems with a large range of user scenarios. Moreover, when 5G is finally realized, the problem will become more complex than that in traditional simplified WSNs. Several challenges related to applying clustering techniques to IoT in 5G environment are presented and discussed.

The use of IoT has become pervasive and IoT devices are common in many domains. Industrial IoT (IIoT) utilises IoT devices and sensors to monitor machines and environments to ensure optimal performance of equipment and processes. Predictive Maintenance (PM) which monitors the health of machines to determine the probable failure of components is one IIoT technique which is receiving attention lately. To achieve effective PM, massive amounts of data are collected, processed and ultimately analysed by Machine Learning (ML) algorithms. Traditionally IoT sensors transmit their data readings to the cloud for processing and modelling. Handling and transmitting massive amounts of data between IoT devices and infrastructure has a cost. Edge Computing (EC) in which both sensors and intermediate nodes can process data provides opportunities to reduce data transmission costs and increase processing speed. This article examines IIoT for PM and discusses how and where data can be processed and analysed. Initially, this article presents sampling and data reduction techniques. These techniques allow for a reduction in the amount of data transmitted to the cloud for processing but there are potential accuracy trade-offs when ML algorithms utilise reduced datasets. An alternative approach is to move ML algorithms closer to the data to reduce data transmission. There are three main techniques that utilise the EC paradigm to perform ML and data processing on intermediary nodes. These techniques are categorized according to where data processing occurs: Device and Edge, Edge and Cloud and Device and Cloud (Federated Learning). In addition to exploring traditional approaches, these three state-of-the-art techniques are examined in this article and their benefits and weaknesses are presented. A novel architecture to demonstrate how EC can be utilized both for data reduction and PM in IIoT is also proposed.

Wireless Sensor Networks (WSNs) are typically composed of thousands of sensors powered by limited energy resources. Clustering techniques were introduced to prolong network longevity offering the promise of green computing. However, most existing work fails to consider the network coverage when evaluating the lifetime of a network. We believe that balancing the energy consumption in per unit area rather than on each single sensor can provide better-balanced power usage throughout the network. Our former work—Balanced Energy-Efficiency (BEE) and its Multihop version BEEM can not only extend the network longevity, but also maintain the network coverage. Following WSNs, Internet of Things (IoT) technology has been proposed with higher degree of diversities in terms of communication abilities and user scenarios, supporting a large range of real world applications. The IoT devices are embedded with multiple communication interfaces, normally referred as Multiple-In and Multiple-Out (MIMO) in 5G networks. The applications running on those devices can generate various types of data. Every interface has its own characteristics, which may be preferred and beneficial in some specific user scenarios. With MIMO becoming more available on the IoT devices, an advanced clustering solution for highly dynamic IoT systems is missing and also pressingly demanded in order to cater for differing user applications. In this paper, we present a smart clustering algorithm (Smart-BEEM) based on our former work BEE(M) to accomplish energy efficient and Quality of user Experience (QoE) supported communication in cluster based IoT networks. It is a user behaviour and context aware approach, aiming to facilitate IoT devices to choose beneficial communication interfaces and cluster headers for data transmission. Experimental results have proved that Smart-BEEM can further improve the performance of BEE and BEEM for coverage sensitive longevity.

The Internet of Things (IoT) involves embedding electronics, software, sensors, and actuators into physical devices, such as vehicles, buildings, and a wide range of smart devices. Network connectivity allows IoT devices to collect and exchange data. The prevalence of IoT devices has increased rapidly in last five years, driven by cheaper electronics and a desire to monitor and control the physical world. We introduce the concept of IoT flood to describe the increased use of IoT devices. Just like the data deluge, the IoT flood has potential benefits and risks. This paper focuses on the hidden side effects of the increased usage of IoT, such as energy consumption, physical pollution, radiation, and health issues. We indicate that an evaluation system with carefully designed metrics reflecting the impact of IoT flood with input from academic, industry, and government is required. We propose some practical measures that can reduce the IoT flood, such as common platforms and data sharing to reduce the side effects. This paper demonstrates the IoT flood problem and potential solutions by examining the intelligent transport system domain where IoT is being deployed to solve problems related to time efficiency and energy consumption through smart mobility.

In the last decade, we have seen an increase in the use of digital technologies in our daily lives, including advanced systems such as mobile applications, smart digital kiosks for intelligent retailing, touch screens for ordering food, or simple implementations such as the ticket machines in the butchers. However, from the perspective of interaction design, those sensing systems suffer from several constraints and limitations such as, low usability and poor hygiene. For example, the elderly or the disabled normally have huge difficulties when interacting with these types of digital systems. Moreover, the recent SARS-CoV-2 pandemic has made us rethink the way we interact with the digital devices. Hence, in this paper, we present a novel solution for digital interaction through a contactless model. This system can provide human gesture recognition and therefore it can be integrated into others to achieve contactless control. We have implemented a prototype based on cost efficient sensors to validate the idea's feasibility. Moreover, a sequence of real world experiments has also been conducted to evaluate its performance. This system is composed with 1) a 3d printed grid of ultrasound sensors to capture the distance information from a human body; 2) software to analyse the data for gesture predictions. This analysis results will be transformed into control commands to interact with the attached system. The experimental results have shown that the proposed system is capable of providing a contactless Human Computer Interaction (HCI), and also has a great opportunity to replace existing touching interaction manner with a remote control scheme.