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.

Wireless sensor network (WSN) is a developed wireless network consisting of some connected sensor nodes. The WSN is employed in many fields such as military, industrial, and environmental monitoring applications. These nodes are equipped with sensors for sensing the environmental variables such as temperature, humidity, wind speed, and so on. In most applications, WSN is positioned in remote places and harsh environments, where they are most probably exposed to faults. Hence, faulty sensor identification is one of the most fundamental tasks to be considered in WSN. This study suggests a hybrid methodology based on mutual information change (MIC) and wavelet transform (WT) for faulty sensor identification. The MIC method is suggested to study correlation among sensors, while the WT technique is proposed for self-sensor detection. WT is suitable for analysing non-stationary signals into approximation and detail coefficients. The suggested algorithm performance is investigated by applying a real case study at an arbitrary location close to Cairo, Egypt. The results of each method are compared using the true positive rate (TPR), false negative rate, and accuracy measures. Obtained results have shown that combining MIC and WT techniques can achieve a higher TPR and accuracy reach 100% in most fault types.

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.

This study explores how wireless ZigBee technology may be applied to automation of electric loads in residential and commercial spaces, allowing to participate in demand response initiatives. The authors discuss development of a custom smart plug with sensing, wireless communication, and electric load actuation capabilities along with several innovative upgrades. There are many commercially available smart plugs that contain multiple sensors and relays. However, very few provide the ability to effectively estimate the proximity between modules or the ability to perform robust system-wide optimisation. The authors propose two innovative smart plug eco-system improvements. One is the use of a received signal strength indicator (RSSI) multi-lateration-based method to estimate the relative proximities of modules. The RSSI values for almost all transmission paths within the ZigBee network are acquired via the authors' forced network reconfiguration algorithm, addressing the limitations of RSSI observation within a star structure. A second innovation is the development of a parallelised neural network training method for application to load automation. The authors use a k-means clustering algorithm to divide training data into subsets such that training may be parallelised.

Wireless sensor network (WSN) comprises of numerous sensors deployed either directly or randomly in the region of interest. Due to the limited power of the sensors, these networks are energy-constrained and thus need efficient power utilisation. Efficient clustering and cluster head (CH) selection ensures balanced energy distribution to the sensors within the WSN and hence prolong the network lifetime. This study proposes the method for evaluating the threshold for the CH selection after each round, which increases the network lifetime and throughput significantly. The threshold for CH selection is modified considering the normalised first-order and second-order statistical parameters, such as mean average low-energy adaptive clustering hierarchy (AvgLEACH) and variance (VarLEACH) of the overall network energy. These methods have been formulated after studying the effect of the number of working nodes in each round on the threshold value selection. Apart from including energy parameter to the threshold equation, the methods of VarLEACH and AvgLEACH are augmented with a residual energy parameter that is local to the nodes and named as VarRLEACH and AvgRLEACH. The simulation results comparing all the methods suggest that the proposed method AvgRLEACH outperforms LEACH by a factor of 1.5 in delivering data to the base station and outlives the network driven by LEACH protocol by 30–40%.