Wireless Sensor Networks (WSN) are enabler technologies for the implementation of the Internet of Things (IoT) concept. WSNs provide an adequate infrastructure for the last-link communication with smart objects. Nevertheless, the wireless communication medium being inherently unreliable, there is the need to increase its communication reliability. Techniques based on the use of cooperative communication concepts are one of the ways to achieve this target. Within cooperative communication techniques, nodes selected as relays transmit not only their own data, but also cooperate by retransmitting data from other nodes. A fundamental step to improve the communication reliability of WSNs is related to the use of efficient relay selection techniques. This paper proposes a relay selection technique based on multiple criteria to select the smallest number of relay nodes and, at the same time, to ensure an adequate operation of the network. Additionally, two relay updating schemes are also investigated, defining periodic and adaptive updating policies. The simulation results show that both proposed schemes, named Periodic Relay Selection and Adaptive Relay Selection, significantly improve the communication reliability of the network, when compared to other state-of-the-art relay selection schemes.

Embedded real-time systems powered by batteries require suitable support for energy-savings at the operating system level. Mechanisms to do so must take into consideration not only energy constraints but also schedulability since, in this kind of system, tasks must execute within predefined time windows. On top of that, it is desired that application quality of service (QoS) is optimized.In this paper we present a framework capable of maximizing application QoS subject to both schedulability and energy constraints. It is assumed that application tasks may have multiple operating modes, each of which exhibiting a specific QoS level when running at a specific processor operating frequency. Although the formulated problem is NP-Hard, experimental analysis has shown that the derived heuristic to solve it achieves very good approximation results and presents low running time.

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We consider the problem of optimizing application quality of service subject to energy and real-time constraints. The system is composed of n sporadic and independent real-time tasks to be scheduled on a single CPU capable of operating at different frequency/voltage levels, which can be selected at run-time. Each task i may run at a selected frequency fi and mode of operation ki with each mode inducing a quality of service level. Due to schedulability and energy bounds, CPU frequencies and task modes should be selected by the system aiming at maximizing the overall system quality of service, an optimization problem formulated as:where QoS represents the considered objective function, and constraints (1b) and (1c) are related to bounds on CPU (ub) and energy (eb), respectively. The set F may contain either the CPU frequency values allowed or represents a continuous frequency interval, a usual frequency model assumed in literature [1]. The set Ki represents a discrete set of modes that task i may operate. Different classes of optimization problems, with distinct degrees of difficulty, can be obtained depending on how F and Ki are modeled. In any case, the objective function QoS must capture some aspects: (I) mode degradation with priority given to the high quality operational modes; (II) task value which is associated with every task being strongly related to the application semantics; (III) task weight specifying the processing resources required by each task, thus a task that requires 10% of CPU should be treated differently from one that requires only 1%, possibly by receiving a higher task weight. Function (2) takes these aspects into account:where Ki = {1, 2, . . .} has the indices of the task modes and wi ∈ [0, 1] is the task value. Mode ki has quality higher than mode k i whenever ki > k i . Notice that QoS will be a value between 0 and 1 since the CPU utilization ui(fi, ki) cannot exceed the unit. The reduction of CPU frequency results in an increase of task execution time which can further delay the completion of the tasks, specially those that need more resources than predicted, a common scenario for soft real-time systems. For this reason the task values should be adjusted depending on the CPU frequency, with higher values for tasks running at high frequency. Assuming that wi = f 2 i , F = {100%, 50%, 25%} and a task with 3 modes, where the utilization at fi = 100% are respectively 0.042, 0.34, 0.60, the QoS function (2) ensures that a configuration with high mode and frequency will always have greater value/system benefit, thereby avoiding unnecessary degradation of the system as shown in Table 1. Table 1: Task configuration benefits QoS(fi, ki) ki = 1 ki = 2 ki = 3 QoS(100%, ki) 0.014 0.448 0.866 QoS(50%, ki) 0.007 0.391 0.766 QoS(25%, ki) 0.003 0.362 0.716 Scheduling problems can be modeled as continuous or discrete as shown in [3]. Continuous and convex models can be efficiently solved by interior-point methods [2]. Discrete models can be solved with relaxation-based heuristics [3,4] or with speciali...

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