The existence of link correlation has been empirically validated, and different exemplary works exploit the link correlation in the designing of various network protocols. In this work, the authors investigated the impact of this link correlation in contention mechanism of medium access control (MAC). They illustrated negative link correlation could deteriorate the contention mechanism designed to handle hidden terminal problems and, consequently, increase the packet collision rate between the neighbours. They also showed that negative link correlation could increase the chance of an exposed terminal problem. Therefore, ignoring negative link correlation could lead to overestimate overall network throughput and underestimate packet delay. Next, instead of designing a new contention mechanism exploiting the link correlation, they proposed a new routing tree which mitigates the negative effect of negative link correlation without altering the underlying MAC layer. They evaluated this routing tree on Indriya testbed with TelosB nodes and compared it to the minimum spanning tree based on link quality only. The results show improvement in end-to-end throughput and packet reception ratio at each node.

In the military battlefield, maintaining high survivability of energy constraint wireless sensor network (WSN) when some nodes are destroyed by the foe troops is one of the prominent issues for the researchers. This study proposes the fuzzy and neuro-fuzzy based relay selection schemes for cooperative WSN which reduce the bit error rate (BER) while improving the network lifetime. Meanwhile, the proposed model maintains the uninterrupted communication between military nodes in a warfare situation. The proposed proposed strategies are compared with the maximum residual energy-based relay selection (MRERS), minimum energy consumption-based relay selection (MECRS), and random relay selection (RRS) strategies. The performance of the proposed and existing strategies is evaluated on the basis of BER, network lifetime, number of dead nodes, and average energy of nodes in the network. The simulation results demonstrate that proposed schemes has 13.19–19.28% improvement in BER than the existing strategies. Moreover, 76.00–90.88% network lifetime improves when compared with the MECRS and RRS strategies. However, 6–7.76% is less than MRERS strategy. Furthermore, the results show that network survivability and average network energy is also better when the number of dead nodes in the network increases.

The exponential rise in the demands of the wireless communication system has alarmed industries to achieve more efficient and quality-of-service (QoS) centric wireless communication networks. The decentralised and infrastructure-less nature of wireless sensor networks (WSNs) enable it to be one of the most sought and used wireless network globally. Its cost-efficiency and functional robustness towards low-power lossy networks make it suitable for internet-of-things (IoT) applications. In recent years, IoT technologies have been used in diverse applications, including Smart City Planning and Management (SCPM). Although, mobile-WSN has played a decisive role in IoT enabled SCPM, its routing optimality and power transmission have always remained challenging. Noticeably, major existing researches address mainly on routing optimisation and very few efforts are made towards dynamic power management (DPM) under non-linear network conditions. With this motive, in this study, a highly robust and efficient QoS – centric reinforcement learning-based DPM model has been developed for mobile-WSN to be used in SCPM. Unlike classical reinforcement learning methods, the authors’ proposed advanced reinforcement learning-based DPM model exploits both known and unknown network parameters and state-activity values, including bit-error probability, channel state information, holding time, buffer cost etc. to perform dynamic switching decision. The key objective of the proposed model is to ensure optimal QoS oriented DPM and adaptive switching control to yield reliable transmission with the maximum possible resource utilisation. To achieve it, they proposed model has been developed as a controlled-Markov decision problem by applying hidden Markov model it obtains known and unknown parameters, which are subsequently learnt using an enhanced reinforcement learning to yield maximum resource utilisation while maintaining low buffer cost, holding cost and bit-error probability to retain the QoS provision.

The accuracy of cooperative localisation can be severely degraded in non-line-of-sight (NLOS) environments. To mitigate the NLOS errors, the cooperative localisation problem based on time of arrival (TOA) under the mixed line-of-sight (LOS)/NLOS conditions is addressed. By studying the topological relationship between nodes, a TOA NLOS mitigation cooperative localisation algorithm based on the topological unit is proposed. This algorithm is implemented under the classical multidimensional scaling framework. The adjacent topological unit of NLOS measurements are successfully identified by using the LOS matrix, and the NLOS measurements are re-estimated using topological units. The least-square method is used to transform the relative coordinates into absolute coordinates depending on the location of the anchor nodes. Compared to the existing methods, by employing the topological unit, this algorithm only requires the number of LOS anchor nodes to be 2 in the two-dimensional plane, and the better localisation performance can be achieved. Simulation results show that the proposed method works well for both the sparse and dense NLOS environments.

In past few years, wireless sensor network (WSN) is considered as an essential and imperative way for efficient data communication in ubiquitous computing environment along with the fulfilment of objectives such as (i) lifetime enhancement and (ii) energy conservation. Till date, the research findings demonstrate that clustering of WSNs is an effective and pertinent approach. Moreover, designing of energy-aware routing schemes for clustered WSNs is a basic necessity due to resource-restricted nature of these sensor nodes. This study has a twofold contribution. First, the research dimensions of WSNs are explained by incorporating recent work carried out as per findings in real scenarios. Secondly, this study presents a comprehensive survey of existing clustering schemes for WSNs based on metaheuristic techniques. This study is beneficial for researchers of this domain as it surveys the literature over the period 2000–2020 on energy efficiency in clustered WSNs.

This paper reports the results of experimental studies of six different wireless sensor nodes and networks under a radiation environment with a dose rate of 20 K Rad (Si)/h. The wireless nodes evaluated are ZigBee, WirelessHART, ISA 100.11a, LoRa, and 433/915 MHz point-to-point devices made from commercial off-the-shelf (COTS) components. The experiments were carried out using a ⁶⁰Co gamma source, while the devices are at on-power operating states, and their operating statuses have been continuously monitored to determine the first instance of failure and the rate of gradual degradation in terms of communication channel performance and quality of the wireless signals. Observations indicate that the different devices and networks exhibit varying levels of radiation tolerance. For example, some can only survive for less than one hour, but others are operating satisfactorily for several hours. Furthermore, before a device suffers a fatal hardware failure, the performance degradation progresses slowly. It is believed that this is the first time that such results are reported in the open literature. Their significance is that the results can provide some practical guidance to select the most suitable wireless devices for the design and construction of remote monitoring systems for high-level radiation environments.

Owing to their short communication range, wireless nodes in wireless sensor networks (WSNs) can exchange information with devices in their vicinity only. Thus, in sparse networks, the full connectivity of the network is rarely achieved. This renders a centralised approach towards localisation in WSNs useless. Moreover, the exploitation of a centralised algorithm for localisation compromises the scalability in dense networks. Thus, a decentralised, location-aware network with partial connectivity and hybrid (range and direction) measurements obtained between known sensors (reference sensors) and sensors at unknown locations (target sensors) is under focus. The decentralised location estimation is obtained using a linear least squares (LLS) approach and performance enhancements are achieved by introducing a weighing strategy to produce weighted least squares (WLS) estimates. This distributed estimation is made possible by designing a map stitching technique that forms the global map of the network from individual local maps of the wireless nodes without compromising the distributed nature of the network. In the analytical section of the study, theoretical mean squares error expression is derived for LLS estimation, and a Cramer–Rao lower bound is derived to bind the performance of the WLS solution. The algorithm's performance validation is conducted both theoretically and via simulations.

IEEE 802.15.4 has been widely accepted as the de facto standard for wireless sensor networks (WSNs). However, as in their current solutions for medium access control (MAC) sub-layer protocols, channel efficiency has a margin for improvement, in this study, the authors evaluate the IEEE 802.15.4 MAC sub-layer performance by proposing to use the request-/clear-to-send (RTS/CTS) combined with frame concatenation and block acknowledgement (BACK) mechanism to optimise the channel use. The proposed solutions are studied in a distributed scenario with single-destination and single-rate frame aggregation. The throughput and delay performance is mathematically derived under channel environments without/with transmission errors for both the chirp spread spectrum and direct sequence spread spectrum physical layers for the 2.4 GHz Industrial, Scientific and Medical band. Simulation results successfully verify the authors’ proposed analytical model. For more than seven TX (aggregated frames) all the MAC sub-layer protocols employing RTS/CTS with frame concatenation (including sensor BACK MAC) allow for optimising channel use in WSNs, corresponding to 18–74% improvement in the maximum average throughput and minimum average delay, together with 3.3–14.1% decrease in energy consumption.

In this study, the authors study binary decision fusion over a shared Rayleigh fading channel with multiple antennas at the decision fusion centre (DFC) in wireless sensor networks. Three fusion rules are derived for the DFC in the case of distributed M-ary hypothesis testing, where M is the number of hypothesis to be classified. Namely, the optimum maximum a posteriori (MAP) rule, the augmented quadratic discriminant analysis (A-QDA) rule and MAP observation bound. A comparative simulation study is carried out between the proposed fusion rules in-terms of detection performance and receiver operating characteristic (ROC) curves, where several parameters are taken into account such as the number of antennas, number of local detectors, number of hypothesis and signal-to-noise ratio. Simulation results show that the optimum (MAP) rule has better detection performance than A-QDA rule. In addition, increasing the number of antennas will improve the detection performance up to a saturation level, while increasing the number of the hypothesis will deteriorate the detection performance.

The idea of aggregate signcryption was first proposed by Selvi. The aggregation process reduces the communication overhead and hence, it is efficient in low-bandwidth communication networks such as wireless sensor networks and vehicular ad-hoc network VANET. The goal of this study is to propose a secure provably identity based aggregate signcryption scheme ID-ASC without pairings over the elliptic curve cryptography. The proposed scheme is provable secure against confidentiality and unforgeability under random oracle model. Moreover, the proposed ID-ASC reduced the computational complexity when compared to other schemes in literature.