The Internet of Things (IoT) is experiencing rapid growth and being adopted across multiple domains. For example, in industry it supports the connectivity needed to integrate smart machines, components and products in the ongoing Industry 4.0 trend. However, there is a myriad of communication technologies that complicate the needed integration, requiring gateways to connect to the Internet. Conversely, using IEEE 802.11 (WiFi) devices can connect to existing WiFi infrastructures directly and access the Internet with shorter communication delays and lower system cost. However, WiFi is energy consuming, impacting autonomy of the end devices. In this work we characterize a recent WiFi-enabled device, namely the ESP8266 module, that is low cost and branded as ultra-low-power, but whose performance for IoT applications is still undocumented. We explore the built-in sleep modes and we measure the impact of infrastructure parameters beacon interval and DTIM period on energy consumption, as well as packet delivery ratio and received signal strength as a function of distance and module antenna orientation to assert area coverage. The ESP8266 module showed suitability for battery powered IoT applications that allow 2-4 days recharge cycles on a 1000mAh battery with seconds-scale transmission intervals.

A growing number of industrial applications incorporate multimedia information processing. These multimedia applications are commonly distributed and subject to time constraints that must be met across networks without creating intolerable interference over typical control flows. However, multimedia traffic, in general, and video streaming, in particular, have specific characteristics that conflict with the operational framework of conventional real-time protocols. In particular, video compressors generate highly variable bit-rate streams that mismatch the constant-bit-rate channels typically provided by real-time protocols, severely reducing the efficiency of network utilization. This paper focuses on low-latency multimedia transmission over Ethernet with dynamic quality-of-service (QoS) management. We propose a multidimensional mechanism that controls, in an integrated way, both the compression parameters and the network bandwidth allocated to each stream. The goal is to provide the best possible QoS to each stream, recomputing the compression levels and network bandwidth whenever significant events, such as channel setup/teardown, or structural changes happen. This paper also presents novel QoS metrics based both on the image quality and network parameters. Several experiments with prerecorded video streams illustrate the advantages of the proposed approach and the convenience of the metrics.

Using low duty-cycle is the most common technique to extend the system lifetime in WSNs. However, it also implies limited throughput and long delay and the penalty is even higher under variable traffic patterns. In this paper, we present iQueue-MAC, a hybrid CSMA/TDMA MAC that adapts to variable/bursty traffic. With light load, iQueue-MAC uses a contention-based CSMA mechanism that provides low delay with scattered transmissions. When traffic increases, detected by a forming backlog in the sender, iQueue-MAC changes to a contention-free TDMA mechanism allocating transmission slots. Thus, iQueue-MAC mitigates packet buffering and reduces packet delay, combining the best of TDMA and CSMA. In this paper we also show how iQueue-MAC can operate in both single and multi channel modes. We implemented it on SIM32W108 chips together with other reference WSN protocols for comparison. iQueue-MAC exhibits similar figures during light traffic. However, with bursty traffic its throughput can be five times that of CoSenS and Ri-MAC-MC and its delay 20 times lower. Finally, iQueue-MAC is able to effectively use multiple channels, duplicating its throughput when compared to single channel operation.

This paper presents a model for quality-of-service (QoS)-aware service composition in distributed systems with real-time and fault-tolerance requirements. This model can be applied in application domains like, for example, remote monitoring, control and surveillance. Classic approaches to real-time systems do not provide the flexibility and fault-tolerance required in new emerging environments that need to combine a high degree of dynamism with temporal predictability. Our approach addresses these new challenges by combining concepts from the service oriented paradigm and distributed real-time systems. We propose a concrete system model based on a holistic time-triggered-based approach for design and configuration. Based on this model, we propose two algorithms for the composition of QoS-aware service-based applications with temporal requirements: an exhaustive algorithm that computes the optimal service combination in terms of a figure of merit, suitable for offline composition; and an improved algorithm based on heuristics and partial figures of merit, suitable for online composition. Experimental results show that the latter reduces dramatically the number of combinations explored with a minimal degradation in the quality of the solution, making it feasible for online execution in dynamic environments.