Outlier detection (OD) constitutes an important issue for many research areas namely data mining, medicines, and sensor networks. It is helpful mainly in identifying intrusion, fraud, errors, defects, noise and so on. In fact, outlier measurements are essential improvements to quality of information, as they are not conforming to expected normal behaviour. Due to the importance of sensed measurements is collected via wireless sensor networks, a novel OD process dubbed density-based spatial clustering of applications with noise (DBSCAN)-OD has been developed based on the algorithm DBSCAN, as a background for OD. With respect to the classic DBSCAN approach, two processes have been jointly combined, the first of computing parameters, while the second concerns class identification in spatial temporal databases. Through both of these modules, one is able to consider real-time application cases as centralised in the base station for the purpose of separating outliers from normal sensors. For the sake of evaluating the authors proposed solution, a diversity of synthetic databases has been applied as generated from real measurements of Intel Berkeley lab. The reached simulation findings indicate well that their devised method can prove to help effectively in detecting outliers with an accuracy rate of 99%.

Haze occurs frequently in many cities, and becomes the authors' great common concern. A distributed real-time monitoring system for atmospheric particles has been designed, implemented and tested. The proposed system consists of a front-end data wireless acquisition network, an embedded web server system, a central server and remote monitoring terminals. The front-end data-acquisition network is made up of mobile data-acquisition nodes distributed in atmospheric particle monitoring areas which collect atmospheric particle data periodically. The data is transmitted by Jennet wireless network and sent to the Internet through the embedded web servers attached to the coordinator nodes via the serial ports. Only a common Internet browser is required for a remote user to check the atmospheric particle data. The central server is responsible for storing the pollution data for further usage of pollution analysis, evaluation and early warning. In this study, the PM1.0, PM2.5 and PM10 particle data were successfully collected, transmitted and checked by the monitoring system in Lanzhou, China. The results show that the designed system can meet the needs of functional requirements of atmospheric particle monitoring.

As a low-cost distance measurement method, received signal strength (RSS) is often used for indoor wireless sensor localisation. However, RSS values can be easily influenced by multi-path fading, noise and other environmental parameters. This decreases the accuracy and stability of estimated distance. To improve localisation accuracy, this study proposes a multiplicative distance-correction factor (MDCF) to counteract the inaccuracy of estimated distance. In the same indoor environment, the product of this CF and estimated distance is regarded as a good approximation of real distance between unknown node and an anchor node. Then, two location estimated methods based on MDCF (MDCF-grid and MDCF-particle swarm optimisation) are proposed. The experimental results confirm that the proposed location estimation methods can significantly improve localisation accuracy without extra hardware in practical indoor scenarios.

One aspect of mobile wireless sensor networks (MWSNs) is the medium access control (MAC) protocol, which is critical in terms of reliability, delay and energy consumption. This work begins with a literature review, showing that the majority of MWSN MACs are designed for hierarchical architectures, demonstrating that there is a lack of MACs intended for flat architecture MWSNs. Subsequently, the authors propose three new MAC protocols, uniquely designed for flat MWSNs. Explicitly, the proposed MACs are carrier sense multiple access with dedicated slots, network division multiple access (NDMA) with collision avoidance and NDMA with dedicated slots, which are specifically designed to work with the state-of-the-art location aware sensor routing protocol. Extensive modelling and simulation are done in dense and sparse scenarios with varying traffic levels to evaluate the impact of the proposed from the perspective of both the link and network layers. Given the uniqueness of the proposed protocols, four well-known MACs are also included to give a performance reference to the results. The MAC results show that the collision-free protocols give the best reliability and that Global time division multiple access consistently yields the highest level of throughput. This highlights the importance of taking into account both MAC and routing during the design process.

Recent hardware developments have made it possible for the Internet of Things (IoT) to be built. A wide variety of industry sectors, including manufacturing, utilities, agriculture, transportation, and healthcare are actively seeking to incorporate IoT technologies in their operations. The increased connectivity and data sharing that give IoT systems their advantages also increase their vulnerability to attack. In this study, the authors explore the automated generation of attacks using genetic programming (GP), so that defences can be tested objectively in advance of deployment. In the authors’ system, the GP-generated attackers targeted publish–subscribe communications within a wireless sensor networks that was protected by an artificial immune intrusion detection system (IDS) taken from the literature. The GP attackers successfully suppressed more legitimate messages than the hand-coded attack used originally to test the IDS, whilst reducing the likelihood of detection. Based on the results, it was possible to reconfigure the IDS to improve its performance. Whilst the experiments were focussed on establishing a proof-of-principle rather than a turnkey solution, they indicate that GP-generated attackers have the potential to improve the protection of systems with large attack surfaces, in a way that is complementary to traditional testing and certification.