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.

Sensing, monitoring and actuating systems are expected to play a key role in reducing buildings overall energy consumption. Leveraging sensor systems to support energy efficiency in buildings poses novel research challenges in monitoring space usage, controlling devices, interfacing with smart energy meters and communicating with the energy grid. In the attempt of reducing electricity consumption in buildings, identifying individual sources of energy consumption is key to generate energy awareness and improve efficiency of available energy resources usage. Previous work studied several non-intrusive load monitoring techniques to classify appliances; however, the literature lacks of an comprehensive system that can be easily installed in existing buildings to empower users profiling, benchmarking and recognizing loads in real-time. This has been a major reason holding back the practice adoption of load monitoring techniques. In this paper we present RECAP: RECognition of electrical Appliances and Profiling in real-time. RECAP uses a single wireless energy monitoring sensor easily clipped to the main electrical unit. The energy monitoring unit transmits energy data wirelessly to a local machine for data processing and storage. The RECAP system consists of three parts: (1) Guiding the user for profiling electrical appliances within premises and generating a database of unique appliance signatures; (2) Using those signatures to train an artificial neural network that is then employed to recognize appliance activities (3) Providing a Load descriptor to allow peer appliance benchmarking. RECAP addresses the need of an integrated and intuitive tool to empower building owners with energy awareness. Enabling real-time appliance recognition is a stepping-stone towards reducing energy consumption and allowing a number of major applications including load-shifting techniques, energy expenditure breakdown per appliance, detection of power hungry and faulty appliances, and recognition of occupant activity. This paper describes the system design and performance evaluation in domestic environment.

Smart farming envisages the harnessing of Information and Communication Technologies as an enabler of more efficient, productive, and profitable farming enterprises. Such technologies do not suffice on their own; rather they must be judiciously combined to deliver meaningful information in near real-time. Decision-support tools incorporating models of disparate farming activities, either on their own or in combination with other models, offer one popular approach; exemplars include GPFARM, APSIM, GRAZPLAN amongst many others. Such models tend to be generic in nature and their adoption by individual farmers is minimal. Smart technologies offer an opportunity to remedy this situation; farm-specific models that can reflect near real-time events become tractable using such technologies. Research on the development, and application of farm-specific models is at a very early stage. This paper thus presents an overview of models within the farming enterprise; it then reviews the state-of the art in smart technologies that promise to enable a new generation of enterprise-specific models that will underpin future smart farming enterprises

The relationship between presence and cognitive factors such as absorption, creative imagination, empathy, and will ingness to experience presence was investigated. Presence was defined, operationalized, and measured using a ques tionnaire that we devised. Absorption and creative imagina tion were measured using questionnaires developed in the area of hypnosis, and empathy was assessed through an interpersonal reactivity index. Results indicated significant correlations between presence and each cognitive factor. They showed that persons who are highly fantasy prone, more empathic, more absorbed, more creative, or more willing to be transported to the virtual world experienced a greater sense of presence. Regression analysis led to a pres ence equation, which could be used to predict presence based on the investigated cognitive factors. Findings are congruent with user characteristics presented by the pres ence literature and support the position that individual dif ferences are important for the study of presence.

Energy efficiency is a central challenge in sensor networks, and the radio is a major contributor to overall energy node consumption. Current energy-efficient MAC protocols for sensor networks use a fixed low-power radio mode for putting the radio to sleep. Fixed low-power modes involve an inherent trade-off: deep sleep modes have low current draw and high energy cost and latency for switching the radio to active mode, while light sleep modes have quick and inexpensive switching to active mode with a higher current draw. This paper proposes adaptive radio low-power sleep modes based on current traffic conditions in the network. It first introduces a comprehensive node energy model, which includes energy components for radio switching, transmission, reception, listening, and sleeping, as well as the often disregarded microcontroller energy component for determining the optimal sleep mode and MAC protocol to use for given traffic scenarios. The model is then used for evaluating the energy-related performance of our recently proposed RFID impulse protocol enhanced with adaptive low-power modes, and comparing it against BMAC and IEEE 802.15.4, for both MicaZ and TelosB platforms under varying data rates. The comparative analysis confirms that RFID impulse with adaptive low-power modes provides up to 20 times lower energy consumption than IEEE 802.15.4 in low traffic scenario. The evaluation also yields the optimal settings of low-power modes on the basis of data rates for each node platform, and provides guidelines and a simple algorithm for the selection of appropriate MAC protocol, low-power mode, and node platform for a given set of traffic requirements of a sensor network application.