IET Wireless Sensor Systems最新文献

Numerous applications require indoor localisation, and one of the current research areas is how to leverage low-cost ubiquitous signals for indoor localisation. This research designs a multi-input convolutional neural network (Multi-CNN) localisation approach to combine natural geomagnetic signals and universal 5G communication signals. To create the location fingerprint data, the geomagnetic three-component data and channel state information (CSI) must first undergo independent preprocessing. Subsequently, the rebuilt CSI amplitude and geomagnetic intensity are employed for separate offline training to efficiently extract the corresponding data features. Lastly, Multi-CNN is used to estimate the user's location online. The localisation outcomes for the conference room and hall demonstrate that the Multi-CNN algorithm can achieve average localisation accuracies of 1.41 and 2.66 m, respectively. These are higher than the single-input CNN algorithms by 21% and 15%, and higher than backpropagation network (BPNN) algorithm by 24% and 17%, and higher than the weighted K-nearest neighbour algorithm by 34% and 28%. The Multi-CNN-based localisation approach successfully fuses the diverse data, potentially satisfying most indoor localisation applications.

Wireless Sensor Network (WSN) localisation refers to the problem of determining the position of each of the agents in a WSN using noisy measurement information. In many cases, such as in distance and bearing-based localisation, the measurement model is a non-linear function of the agents' positions, leading to pairwise interconnections between the agents. As the optimal solution for the WSN localisation problem is known to be computationally expensive in these cases, an efficient approximation is desired. The authors show that the inherent sparsity in this problem can be exploited to greatly reduce the computational effort of using an Extended Kalman Filter (EKF) for large-scale WSN localisation. In the proposed method, which the authors call the L-Banded Extended Kalman Filter (LB-EKF), the measurement information matrix is converted into a banded matrix by relabelling (permuting the order of) the vertices of the graph. Using a combination of theoretical analysis and numerical simulations, it is shown that typical WSN configurations (which can be modelled as random geometric graphs) can be localised in a scalable manner using the proposed LB-EKF approach.

Poisson multi-Bernoulli Mixture (PMBM) filter has been known as an available or practical point and multiple extended target tracking (METT) method. The authors present an improved PMBM filter with adaptive detection probability and adaptive newborn distributions, accompanying with an associated distributed fusion strategy for the tracking extended multiple targets. First, the augmented state of unknown and changing target detection probability is assumed as Gamma (GAM) distribution. Second, extended states are described by Inverse Wishart (IW) distribution based on this augmented state, accompanying with dynamic states presented by Gaussian distribution. And then, an adaptive newborn distribution is adopted to describe the newborn targets appearing arbitrarily. Consequently, the closed-form solutions of the proposed filter can be derived by approximating the intensity of newborn and potential targets to the Gamma Gaussian Inverse Wishart (GGIW) form. Moreover, the fused means that Generalised Covariance Intersection (GCI) is performed in such a large-scale aquaculture sensor network. Experiments are presented to verify the availability of the GCI-GGIW-PMBM method, and comparisons with other METT filters also demonstrate that tracking behaviours are improved largely.

The Internet of Things (IoT) is a growing technology that remotely connects multiple devices (ranging across many fields and applications) over the Internet. The scalability of an IoT network mandates a reliable transport infrastructure. Traditional transport control protocol (TCP) control protocol is unsuitable for such domain, mainly due to energy and power consumption reasons. A lighter version of TCP, light weight IP (lwIP) provides a promising solution for current and projected future scalable IoT infrastructures. However, the original lwIP is just a simple mapping of the protocol, without insight into the IoT specific requirements. This paper examines the lwIP congestion control mechanism and addresses its shortcomings. In particular, a detailed examination is devoted to the various metrics such as retransmission time-outs and its back-off epochs, the congestion window behaviour and progress in the absence (and presence) of congestion. In particular, we propose a set of novel algorithms to address both the IoT constraints nature (light-weight) as well as keeping up with scalability in IoT network size and performance. A detailed simulation study has been conducted to endorse the viability of our proposed set of algorithms for next-generation IoT networks.

Internet of Things is an emerging paradigm based on interconnecting physical and virtual objects with each other and to the Internet. Most connected things fall into the category of constrained devices, with restricted resources (processing power, memory, and energy). These low-power and lossy networks (LLNs) are known for their instability, high loss rates and low data rates, which makes routing one of the most challenging problems in low-cost communications. A routing protocol for low-power and lossy networks (RPL) is a proactive dynamic routing protocol based on IPv6. This protocol defines an objective function (OF) that utilises a set of metrics to select the best possible path to the destination. Minimum rank hysteresis objective function (MRHOF) and objective function zero (OF0) are the most basic OFs, where the first one selects the path to the sink based on the expected transmission count (ETX) metric, and OF0 is based on the hop count (HC). These two metrics prioritise either brute performance (i.e. ETX) or simplicity (i.e. HC). Therefore, using a single metric with an OF can either limit the performance or have an inefficient impact on load management and energy consumption. To overcome these challenges, a routing metric based on MRHOF OF which takes into consideration the link-based routing metric (i.e. ETX) and node-based metric (i.e. remaining energy) for route selection is provided. Expected transmission count remaining energy (ETXRE) is evaluated through 36 scenarios with different parameters. Preliminary results show that ETXRE outperforms ETX and RE in terms of end-to-end delay by an average of at least 17%, packet delay by 13% and consumes 10% less energy.

Extensive use of the Internet of Things (IoT) in smart homes makes users' lives easy and comfortable. Yet, these resource-constrained devices are prone to manifold security attacks. The sinkhole attack is one of the most destructive attacks that disrupt smart home operations, causing user dissatisfaction. Existing intrusion detection systems (IDS) cannot handle sinkhole attacks competently as they (i) do not consider the node capacity for being an IDS agent, leading to a low attack detection ratio, (ii) do not examine the sinkhole node's role when mitigating attacks, causing remaining network disconnection with the root node and (iii) do not consider replacing energy-exhausted IDS nodes, causing connectivity loss of partial network with the root. This paper addresses these shortcomings and adequately presents a mechanism to handle sinkhole attacks. A formulation for assigning weights to network nodes based on their resources is proposed here. An IDS placement strategy is introduced to place IDS agents on particular resourceful nodes that extend network lifetime and enhance attack detection capability. We present a novel attack detection and mitigation strategy by ensuring network connectivity. The proposed mechanism achieves 95% attack detection accuracy and reduces false negative rates by 25% and energy consumption reasonably compared to the state-of-the-art.

In a mobile ad hoc network (MANET), all nodes are communicated with one another across wireless networks to create a temporary network without the support of centralised management. Due to dynamic topology in MANET, secure routing is a crucial issue. The existing secure routing protocol and their security concerns are analysed in this work. The suggested Swarm Intelligence-based Secure Ad-hoc On-demand Distance Vector (SIS-AODV) algorithm offers security by applying a secret key and hash mechanism to prevent the involvement of malicious nodes in routing operations. A secure routing system of MANET guards against internal and external network attacks. The proposed SIS-AODV algorithm consists of two sections: the secret key value generated by applying Elliptical Curve Cryptography (ECC)-based algorithm and the PRESENT algorithm to encrypt the data packets. Besides, authentication and non-repudiation are applied using the H-PRESENT 128 algorithm. The PRESENT algorithm and H-PRESENT 128 hash function require less computational power. Centralised management is optional in this scheme, so overhead decreases. The second section of SIS-AODV consists of Ant Colony Grey Wolf Optimization over the AODV algorithm to improve network performance while implementing a security algorithm over MANET. Analysis results show maximum performance with a packet delivery ratio of 98% and throughput of 85%. In addition, end-to-end delay is reduced by up to 25%, and routing overhead decreases by up to 20%. Keywords: AODV, Elliptical Curve, PRESENT, H-PRESENT, Euclidean Algorithm, ACO, GWO, Blackhole attack.

A closed-form solution for localising and synchronising an acoustic sensor node with respect to a Wireless Acoustic Sensor Network (WASN) is presented. The aim is to allow efficient scaling of a WASN by individually calibrating newly joined sensor nodes instead of recalibrating the entire array. A key contribution is that the sensor to be calibrated does not need to include a built-in emitter. The proposed method uses signals emitted from spatially distributed sources to compute time difference of arrival (TDOA) measurements between the existing WASN and a new sensor. The problem is then modelled as a set of multivariate non-linear TDOA equations. Through a simple transformation, the non-linear TDOA equations are converted into a system of linear equations. Then, weighted least squares is applied to find an accurate estimate of the calibration parameters. Signal sources can either be known emitters within the existing WASN or arbitrary sources in the environment, thus allowing for flexible applicability in both active and passive calibration scenarios. Simulation results under various conditions show high joint localisation and synchronisation performance, often compared to the Cramér-Rao lower bound.

Communication architectures based on the Internet of Things (IoT) are increasingly frequent. Commonly, these solutions are used to carry out control and monitoring activities. It is easy to find cases for manufacturing, prediction maintenance, Smart Cities, etc., where sensors are deployed to capture data that is sent to the cloud through edge devices or gateways. Then that data is processed to provide useful information and perform additional actions if required. As crucial as deploying these monitoring solutions is to verify their operation. In this article, we propose a novel warning method to monitor the performance of IoT-based systems. The proposal is based on a holistic quality model called Quality of X (QoX). QoX refers to the use of a variety of metrics to measure system performance at different quality dimensions. These quality dimensions are data (Quality of Data, QoD), information (Quality of Information, QoI), users' experience (Quality of user Experience, QoE), and cost (Quality Cost, QC). In addition to showing the IoT system performance in terms of QoX in real-time, our proposal includes (i) a forecasting model for independent estimation of QoX applying Deep Learning (DL), specifically using a Long Short-Term Memory (LSTM) and time series, and (ii) the warning system. In light of our results, our proposal shows a better capacity to forecast quality drops in the IoT-based monitoring system than other solutions from the related literature.

The trajectory localisation of human activities using signal analytics has become a reality due to the widespread use of advanced signal processing systems. Device-free localisation using WiFi devices is prevalent, and the received signal strength indicator (RSSI) and channel state information (CSI) signals offer additional benefits. However, radio frequency (RF) localisation is highly dependent on the environment, so updating fingerprint data is necessary by changing the environment. This work presents Fine-grained Indoor Detection and Angular Radar for recognising and locating humans using a multipath trajectory reflections system that does not require training. It estimates location using a probabilistic approach that considers changes in CSI and RSSI across multiple nodes, generating an informative dataset that reflects the current trajectory and status of the location. The presented method extracts data from clustered Raspberry Pi 4B and Nexmon. The method exhibits a versatile real-time location-tracking solution by utilising the distinctive properties of RF signals. This technology has significant implications for various applications, including human medical monitoring, gaming, smart cities, and optimising building layouts to improve efficiency. The model demonstrates location-independent localisation with up to 80% accuracy in mapping trajectories at any location. The findings indicate that the proposed model is effective and reliable for indoor localisation and activity tracking, making it a promising solution for implementation in real-world environments.