The Internet of Things (IoT) is attracting considerable attention from the universities, industries, citizens and governments for applications, such as healthcare, environmental monitoring and smart buildings. IoT enables network connectivity between smart devices at all times, everywhere, and about everything. In this context, Wireless Sensor Networks (WSNs) play an important role in increasing the ubiquity of networks with smart devices that are low-cost and easy to deploy. However, sensor nodes are restricted in terms of energy, processing and memory. Additionally, low-power radios are very sensitive to noise, interference and multipath distortions. In this context, this article proposes a routing protocol based on Routing by Energy and Link quality (REL) for IoT applications. To increase reliability and energy-efficiency, REL selects routes on the basis of a proposed end-to-end link quality estimator mechanism, residual energy and hop count. Furthermore, REL proposes an event-driven mechanism to provide load balancing and avoid the premature energy depletion of nodes/networks. Performance evaluations were carried out using simulation and testbed experiments to show the impact and benefits of REL in Sensors 2013, 13 1943 small and large-scale networks. The results show that REL increases the network lifetime and services availability, as well as the quality of service of IoT applications. It also provides an even distribution of scarce network resources and reduces the packet loss rate, compared with the performance of well-known protocols.

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Wireless Multimedia Sensor Networks (WMSNs) play an important role in pervasive and ubiquitous systems. WMSNs promise a wide scope of potential applications in both civilian and military areas, which require visual and audio information such as environmental monitoring, smart parking, traffic control, and other applications for smart cities. The multimedia content in such applications has the potential to enhance the level of collected information, show the real impact of the event and help to detect objects or intruders. However, WMSN applications must assure reliability, scalability, energy-efficiency and quality level (also from the user's point-of-view) to support the transmission of multimedia content. With this goal in mind, this article outlines a smart Multi-hop hierarchical routing protocol for Efficient VIdeo communication over WMSN (MEVI). MEVI combines a cluster formation scheme with a minimal signaling overhead, a cross-layer solution to select routes based on network conditions and energy issues, and a smart scheme to trigger multimedia transmission according to sensed data. The cluster approach aims to minimize the energy consumption and is suitable for the distribution of multimedia content in WMSNs.

For smart applications, nodes in wireless multimedia sensor networks (MWSNs) have to take decisions based on sensed scalar physical measurements. A routing protocol must provide the multimedia delivery with quality level support and be energyefficient for large-scale networks. With this goal in mind, this paper proposes a smart Multi-hop hierarchical routing protocol for Efficient VIdeo communication (MEVI). MEVI combines an opportunistic scheme to create clusters, a cross-layer solution to select routes based on network conditions, and a smart solution to trigger multimedia transmission according to sensed data. Simulations were conducted to show the benefits of MEVI compared with the well-known Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol. This paper includes an analysis of the signaling overhead, energy-efficiency, and video quality.

Location-Based Social Networks (LBSN) data contains spatial, temporal, and social features of user activity, providing valuable information that is currently available on large-scale and low-cost fashion via traditional data collection methods. In this way, LBSN data enables to predict user mobility based on spatial, temporal, and social features, which can be used in several areas, such as device-to-device (D2D) communication, caching, and others. In addition, a Temporal Markov Chain (TMC) is a stochastic model used to model randomly changing systems, such as mobility prediction based on the spatiotemporal factor such as location and day of the week. In this paper, we introduce the Temporal Markov Model with User Similarity (TEMMUS) mobility prediction model. TEMMUS considers a TMC of variable order based on the day of the week (weekday or weekend) and the user similarity to predict the user's future location. The results highlight a higher accuracy of TEMMUS compared to three state-of-the-art Markov Model predictors.