The demand for smart environment technologies necessitates technical innovations in sensor networks. It is now well expected that smart sensor technologies will soon be deployed for a wide variety of applications. Interest in applications of sensor networks will keep growing and will create a new class of industrial standardizations in various different application domains. In this context, the papers in this special section cover a number of novel contributions related to energy efficient PHY-aided MAC techniques and sensor applications. Targeted are recent trends, research outcomes, practical developments of sensor infrastructure, reliable MAC solutions leveraging advanced PHY-layer aspects, and internet-of-things (IoT) solutions as well as compact and low-power sensor device technologies, which are applied to challenge measurement problems and applications.

It is now well expected that low-power sensor networks will soon be deployed for a wide variety of applications. These networks could potentially have millions of nodes spread in complex indoor/outdoor environments. One of the major deployment challenges under such diverse communication environments is providing reliable communication links to those low cost and/or battery-powered sensor nodes. Over the past few years, research in physical (PHY)-layer has demonstrated promising progresses on link reliability and energy efficiency. In modern medium access control (MAC) design, energy efficiency has become one of the key requirements and is still a hot research topic. In this overview, we provide a broad view encompassing both PHY- and MAC-layer techniques in the field of sensor networks with a focus on link reliability and energy efficiency. We review work in systems employing various PHY techniques in spatial diversity, energy efficient modulation, packet recovery, and data fusion, as well as MAC protocols in contention-based duty cycling, contention-free duty cycling, and hybrid duty cycling. The latest developments in cross-layer MAC designs that leverage PHY-layer techniques are presented. We also provide a synopsis of recent development and evolution of sensor network applications in industrial communications.

Low power sensor networks with communication enabled by WiFi are expected to be widely deployed. A major challenge is collecting event-driven uplink data from a large number of low-power sensors with low latency. In WiFi, the access point (AP) typically polls nodes individually to schedule uplink transmission times, resulting in a large latency. In this paper, we present a physical (PHY) layer-aided medium access control (MAC) framework to enhance the uplink throughput of sensor data traffic. In the approach, the acknowledgements from the sensor nodes to the poll message are parallelized. By detecting the parallel acknowledgement, the AP knows which nodes have data to send and allocates channel resources by sending a pull message. This approach is referred to as the probe and pull MAC (PPMAC) mechanism. Our scheme is based on maximizing the achievable throughput of PPMAC by optimizing the PHY layer components. More precisely, we investigate the parallel acknowledgement detector design problem and develop a non-convex optimization framework that maximizes the PPMAC throughput by optimizing the parallel acknowledgement detection statistics. Numerical examples illustrate that PPMAC outperforms the point coordination function (PCF) and distributed coordination function (DCF) mechanisms, standardized in IEEE 802.11, in terms of the achievable throughput and the overhead.

A major challenge in low power sensor networks is collecting data from large number of nodes with low latency. Access point (AP) typically needs to poll large number of nodes individually and schedule transmission times for each of the nodes resulting in large latency. In this paper, we propose a physical layer technique that parallelizes the polling response from multiple nodes (so called parallel acknowledgment) in order to decrease the latency. Each node responds to the poll request of an AP by using the well-known Zadoff-Chu (ZC) sequences. By using the orthogonal properties of the ZC sequences, the AP is able to resolve the multiple signatures of different nodes simultaneously. We further develop low complexity transmission scheme for the ZC sequences in order to reduce the number of computations and complexity required by the low power sensor nodes. We also compare the performance of both time and frequency domain receivers.

Wireless sensor nodes are increasingly being tasked with computation and communication intensive functions while still subject to constraints related to energy availability. On these embedded platforms, once all low power design techniques have been explored, duty-cycling the various subsystems remains the primary option to meet the energy and power constraints. This requires the ability to provide spurts of high MIPS and high bandwidth connections. However, due to the large overheads associated with duty-cycling the computation and communication subsystems, existing high performance sensor platforms are not efficient in supporting such an option. In this article, we present the design and optimizations taken in a wireless gateway node (WGN) that bridges data from wireless sensor networks to Wi-Fi networks in an on-demand basis. We discuss our strategies to reduce duty-cycling related costs by partitioning the system and by reducing the amount of time required to activate or deactivate the high-powered components. We compare the design choices and performance parameters with those made in the Intel Stargate platform to show the effectiveness of duty-cycling on our platform. We have built a working prototype, and the experimental results with two different power management schemes show significant reductions in latency and average power consumption compared to the Stargate . The WGN running our power-gating scheme performs about six times better in terms of average system power consumption than the Stargate running the suspend-system scheme for large working-periods where the active power dominates. For short working-periods where the transition (enable/disable) power becomes dominant, we perform up to seven times better. The comparative performance of our system is even greater when the sleep power dominates.