The initial vision of the Internet of Things (IoT) was of a world in which all physical objects are tagged and uniquelly identified by RFID transponders. However, the concept has grown into multiple dimensions, encompassing sensor networks able to provide real-world intelligence and goal-oriented collaboration of distributed smart objects via local networks or global interconnections such as the Internet. Despite significant technological advances, difficulties associated with the evaluation of IoT solutions under realistic conditions, in real world experimental deployments still hamper their maturation and significant roll out. In this article we identify requirements for the next generation of the IoT experimental facilities. While providing a taxonomy, we also survey currently available research testbeds, identify existing gaps and suggest new directions based on experience from recent efforts in this field.

Despite national plans to deploy smart meters in small and medium businesses in the UK, there is little knowledge of occupant energy use in offices. The objectives of the study were to investigate the effect of individual feedback on energy use at the workdesk, and to test the relationship between individual determinants, energy use and energy reduction. A field trial is presented, which monitored occupant energy use and provided individual feedback to 83 office workers in a university. The trial comprised pre- and post-intervention surveys, energy measurement and provision of feedback for 18 weeks post-baseline, and two participant focus groups. The main findings were: statistically significant energy reduction was found, but not for the entire measurement period; engagement with feedback diminished over time; no measured individual variables were related to energy reduction and only attitudes to energy conservation were related to energy use; an absence of motivation to undertake energy reduction actions was in evidence. The implications for energy use in offices are considered, including the need for motivations beyond energy reduction to be harnessed to realise the clear potential for reduced energy use at workdesks. © 2013 The Authors

Abstract-To optimize the energy utilization, intelligent energy management solutions requires appliance-specific consumption statistics. One can obtain such information by deploying smart power outlets on every device of interest; however it incurs extra hardware cost and installation complexity. Alternatively, a single sensor can be used to measure total electricity consumption and thereafter disaggregation algorithms can be applied to obtain appliance specific usage information. However, discerning lowpower appliances in the presence of high-power loads is quite challenging. To improve the recognition of low-power appliance states, we propose a solution that makes use of circuit-level power measurements. We examine the use of a specialized variant of Hidden Markov Model (HMM) known as Factorial HMM (FHMM) to recognize appliance specific load patterns from the aggregated power measurements. Further, we demonstrate that feature concatenation can improve the disaggregation performance of the model allowing it to identify device states with an accuracy of 90% and 80% for binary and multi-state appliances respectively. Through experimental evaluations, we show that our solution performs better than the traditional event based approach. In addition, we develop a prototype system that allows real-time monitoring of appliance states.

This paper presents ALBA-R, a protocol for convergecasting in wireless sensor networks. ALBA-R features the cross-layer integration of geographic routing with contention-based MAC for relay selection and load balancing (ALBA), as well as a mechanism to detect and route around connectivity holes (Rainbow). ALBA and Rainbow (ALBA-R) together solve the problem of routing around a dead end without overhead-intensive techniques such as graph planarization and face routing. The protocol is localized and distributed, and adapts efficiently to varying traffic and node deployments. Through extensive ns2-based simulations, we show that ALBA-R significantly outperforms other convergecasting protocols and solutions for dealing with connectivity holes, especially in critical traffic conditions and low-density networks. The performance of ALBA-R is also evaluated through experiments in an outdoor testbed of TinyOS motes. Our results show that ALBA-R is an energy-efficient protocol that achieves remarkable performance in terms of packet delivery ratio and end-to-end latency in different scenarios, thus being suitable for real network deployments

Neighbor discovery was initially conceived as a means to deal with energy issues at deployment, where the main objective was to acquire information about network topology for subsequent communication. Nevertheless, over recent years, it has been facing new challenges due to the introduction of mobility of nodes over static networks mainly caused by the opportunistic presence of nodes in such a scenario. The focus of discovery has, therefore, shifted toward more challenging environments, where connectivity opportunities need to be exploited for achieving communication. In fact, discovery has traditionally been focused on tradeoffs between energy and latency in order to reach an overlapping of communication times between neighboring nodes. With the introduction of opportunistic networking, neighbor discovery has instead aimed toward the more challenging problem of acquiring knowledge about the patterns of encounters between nodes. Many Internet of Things applications (e.g., smart cities) can, in fact, benefit from such discovery, since end-to-end paths may not directly exist between sources and sinks of data, thus requiring the discovery and exploitation of rare and short connectivity opportunities to relay data. While many of the older discovery approaches are still valid, they are not entirely designed to exploit the properties of these new challenging scenarios. A recent direction in research is, therefore, to learn and exploit knowledge about mobility patterns to improve the efficiency in the discovery process. In this paper, a new classification and taxonomy is presented with an emphasis on recent protocols and advances in this area, summarizing issues and ways for potential improvements. As we will show, knowledge integration in the process of neighbor discovery leads to a more efficient scheduling of the resources when contacts are expected, thus allowing for faster discovery, while, at the same time allowing for energy savings when such contacts are not expected.INDEX TERMS Neighbour discovery, opportunistic networking, Internet of Things, mobility, knowledge.