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This paper proposes an iterative power allocation algorithm that maximizes the energy efficiency of a multiple-relay cooperative multiple antenna (MIMO) network. Energy efficiency is defined as the achievable spectral efficiency normalized by the total power consumption, which also accounts for the RF circuitry consumption, modeled in a wireless sensor network. Moreover, relay selection is performed using the distributed opportunistic selection protocol, while we derive closed-form expressions for outage probability and energy efficiency for a single antenna (SISO) and two MIMO scenarios: 1) based on antenna selection (AS) and 2) based on single value decomposition (SVD) beamforming. In order to evaluate the performance of the proposed algorithm, we compare it to an exhaustive search optimal power allocation method. Results show that the proposed algorithm performs very close to an exhaustive search approach and that, although MIMO SVD has the best outage performance, AS has the best energy efficiency for low and moderate source-destination distances and spectral efficiencies. [ABSTRACT FROM AUTHOR]

In this paper we analyze the energy efficiency of single-hop, multi-hop, cooperative selective decode-and-forward, cooperative incremental decode-and-forward, and even the combination of cooperative and non-cooperative schemes, in wireless sensor networks composed of several nodes. We assume that, as the sensor nodes can experience either non line-of-sight or some line-of-sight conditions, the Nakagami-m fading distribution is used to model the wireless environment. The energy efficiency analysis is constrained by a target outage probability and an end-to-end throughput. Our results show that in most scenarios cooperative incremental schemes are more energy efficient than the other methods. [ABSTRACT FROM AUTHOR]

The effective deployment of Internet of Things (IoT) applications such as smart cities, smart farming and smart transport systems must ensure the network robustness, scalability and longevity. Therefore, guaranteeing the successful delivery of information and extending the lifetime of the nodes that make up a wireless sensor network (WSN) are two essential aspects for IoT applications. This work evaluates the performance of a cooperative WSN by adopting two multiantenna schemes: antenna selection (AS) and beamforming transmission using the singular value decomposition (SVD) technique. In addition, cooperation is established according to an ON–OFF probability, so that the RF receiving circuits of the relays are activated in a probabilistic way, aiming at reducing the energy consumption of the sensors, extending their useful lifetime. Our main goal is to increase the amount of information effectively transmitted by the network, keeping an outage probability constraint. The results show that, when both techniques are used, there is a significant gain in the amount of information effectively transmitted by the network, with emphasis on the AS scheme at short transmission distances. By increasing the number of antennas, it was found that a lower ON–OFF probability is required, i.e., a trade-off is established between the nodes' hardware complexity and their need for cooperation. [ABSTRACT FROM AUTHOR]

We compare the energy efficiency of some transmission schemes in wireless sensor networks. By constraining the system to end-to-end throughput requirements, we assume that the nodes in multi-hop and cooperative schemes operate at a spectral efficiency which is twice that of the single-hop transmission. By taking into account the energy consumption of the RF circuitry, we show that cooperation may be considerably more energy efficient than non-cooperative schemes even in small transmission ranges, specially if a feedback channel is available. [ABSTRACT FROM AUTHOR]

Wireless Sensors Networks (WSNs) are an essential element of the Internet of Things (IoT), and are the main producers of big data. Collecting a huge amount of data produced by a resource-constrained network is a very difficult task, presenting several challenges. Big data gathering involves not only periodic data sensing, but also the forwarding of queries and commands to the network. Conventional network protocols present unfeasible strategies for large-scale networks and may not be directly applicable to IoT environments. Information-Centric Networking is a revolutionary paradigm that can overcome such big data gathering challenges. In this work, we propose a soft-state information-centric protocol, ICENET (Information Centric protocol for sEnsor NETworks), for big data gathering in large-scale WSNs. ICENET can efficiently propagate user queries in a wireless network by using a soft-state recovery mechanism for lossy links. The scalability of our solution is evaluated in different network scenarios. Results show that the proposed protocol presents approximately 84% less overhead and a higher data delivery rate than the CoAP (Constrained Application Protocol), which is a popular protocol for IoT environments. [ABSTRACT FROM AUTHOR]