Due to their computational complexity, iterative decoder components such as source and channel decoders are usually implemented using specialised dedicated hardware. This leads to the scenario where each different iterative decoder component of the receiver requires its own hardware. Due to their relatively high complexity, many capacity-approaching techniques proposed in the literature have not yet been invoked in wireless sensor network applications, despite their potential benefits of facilitating a reduced transmission power or extended communication range. Against this background, the authors propose an energy-efficient architecture comprised of multiple computation unit (CU), which is sufficiently flexible for accommodating different iterative decoder components using the same hardware. In this study, the flexible architecture is applied to Joint Source and Channel Coding (JSCC), comprising the Unary Error Correction (UEC) code, a turbo code and an iterative demodulator. The authors conceive a flexible technique for controlling the hardware, which supports a high hardware-exploitation ratio for the CU, reaching a utilisation of 88%, compared with 68% achieved in similar solutions reported in the open literature.

Speed measurements are crucial for controlling traffic and in supporting automation of industrial processes. The speed of moving objects is typically measured from a stationary position using time-of-flight or the Doppler effect. Existing approaches require either relatively bulky physical devices or complex signal processing. The authors propose a new method for measuring the speed of (metal) objects moving on a fixed track. The authors’ method is applicable, e.g. to cars on a road, trains on rails, or goods sliding on a conveyor belt. The method relies on the constructive and destructive interference patterns created by the reflections from the moving target. Speed is determined by measuring the signal strength of received messages as perceived by standard wireless devices. The method requires only minimal signal processing and only two commodity wireless transceivers, which are independent of the moving target. The advantages of their system compared to other technologies are reduced size, reduced cost, and in some scenarios robustness.

Large applications of sensor networks, such as environmental risk monitoring, require the deployment of hundreds or even thousands of nodes. This study proposes and implements a novel stochastic physics-based optimisation algorithm that is both efficient (guarantees full target coverage with a reduced number of sensors) and scalable (meaning that it can be executed for very large-scale problems in a reasonable computation time). The algorithm employs ‘virtual sensors’ which move, merge, recombine, and ‘explode’ during the course of the algorithm, where the process of merging and recombining virtual sensors reduces the number of actual sensors while maintaining full coverage. The parameters which control sensor merging and explosion are varied during the algorithm to perform the same function as an annealing schedule in simulated annealing. Simulation results illustrate the rapidity and the effectiveness of the proposed method.