This work presents a combined energy-efficient medium access control (MAC) and routing protocol for large-scale wireless sensor networks that aims to minimise energy consumption and prolong the network lifetime. The proposed communication framework employs the following measures to enhance the network energy efficiency. Firstly, it provides an adaptive intra-cluster schedule to arbitrate media access of sensor nodes within a cluster, minimising idle listening on sensor nodes, leading to improved energy performance. Secondly, it proposes an on-demand source cross-layer routing protocol ensuring selection of best routes based on energy level and channel quality indicator for the multihop inter-cluster data transmission. Lastly, an unequal cluster size technique based on cluster head residual energy and distance away from the base station is utilised. This technique balances the energy among clusters and avoids early network partitioning. This work further presents the analytical performance model for energy consumption and delay of the proposed communication framework. The performance measures used for evaluation are energy consumption, delay, and network lifetime. The results indicate that combining routing and MAC schemes conserves energy better than utilising MAC scheme alone.

In wireless sensors networks, the sensor nodes are densely deployed. Owing to this excessive deployment of sensor nodes, each target is covered by multiple sensors at a time. To prolong the network lifetime, the authors can schedule the sensor activity in such a way that only a subset of sensor nodes, called cover set, is sufficient enough to cover all the targets. In this study, they propose an energy-efficient scheduling algorithm based on learning automata for target coverage problem. The learning automata-based technique helps a sensor node to select its appropriate state (either active or sleep). To prove the effectiveness of their proposed scheduling method, they conduct a detailed set of simulations and compare the performance of their algorithm with the existing algorithms.

Energy efficiency is an important requirement in wireless sensor networks in order to achieve cost-effectiveness and practical implementation. The present work deals with the problem of minimising node power consumption in the context of moving-node localisation and tracking. Time-of-arrival measurements are sent from anchor nodes to a powerful, usually sophisticated, central node, called the fusion centre, where all computations are performed. Low data rates are desirable to economise on node energy but result in sub-optimal localisation accuracy. It makes sense, therefore, to sample measurements at a low data rate while interpolating the data stream at the fusion centre to improve localisation. The localisation error is remarkably reduced and energy efficiency increased by using this conventional sample rate conversion technique. A further improvement in terms of localisation error is achieved using compressive sensing (via random sampling and interpolation), whereby the localisation error function is shown to decrease with higher-average random sampling periods.

Cognitive Wireless Sensor Networks (CWSNs) provide better bandwidth utilization when compared with normal wireless sensor networks. CWSNs use a technique called opportunistic spectrum access for data transfer. While doing so, however, CWSNs are subject to several security threats. The spectrum sensing data falsification attack comes under the DoS attack. In this attack, a malicious node sends a modified spectrum sensing report so that the resulting collaborative spectrum sensing decision becomes wrong and a good cognitive sensor node receives a wrong decision regarding the vacant spectrum band of other's network. In the presence of the node cloning attack, the solution of the SSDF attack becomes even more difficult. In the node cloning attack, the malicious node creates many clones of the compromised node in the network. In order to confuse the collaborative spectrum sensing system, the clone nodes can send false spectrum sensing reports in a large number. The maximum-match filtering (MMF) algorithm is used for making a secure spectrum sensing decision in CWSNs. The Cloned-Node Detection (CND) algorithm is proposed here to detect cloned nodes. This study also explains how the CND algorithm assists the MMF algorithm to make better spectrum sensing decisions by avoiding the node cloning attack.

In wireless sensor network, sensors are deployed to sense useful data from environment. Sensors are energy-constrained devices. To prolong the sensor network lifetime for large-scale network, nowadays mobile data sinks (MDSs) (collectors/mules) are used for collecting the sensed data from the sensors. In this environment, sensor nodes can directly transfer their sensed data to the MDS. Sensors have limited memory. Therefore, to avoid buffer overflow, the data must be collected by the MDSs within a predefined time interval. The authors assume that a set of mobile sensors are moving arbitrarily on a set of paths. The authors objective is to collect data periodically from all mobile sensors using minimum number of MDSs within a fixed time interval. Here, a data-gathering algorithm is proposed. The authors analyse the complexity of the problem, and evaluate time complexity and performances of the proposed solution.

Node localisation technology is one of the most challenging and important issues in wireless sensor networks. This study puts forward a novel and high accuracy estimation method by combining distance-measuring with angle-measuring. An anchor node equipped with a directional antenna periodically sends beacons containing its position and antenna orientation to unknown nodes. By observing the variation of received signal strength indication values of beacons and the time of flight values, an unknown node can estimate the distance and orientation relative to the beacon node simultaneously. This study proposed an online modelling method to calibrate the shift of the estimated distance. Meanwhile, an angle estimation method is presented to avoid an ambiguous result, which is a new weighted curve fitting method based on least square principle. The experimental results show that the proposed ranging and angle measurement reaches a better localisation accuracy compared with the original ranging and angle method.