International Journal of Sensor Networks (11 papers in press)

Regular Issues

• Preserving privacy in Mobile Crowdsensing  
  by Bayan H. Alamri, M.M. Monowar, Suhair Alshehri, Haseeb Zafar, Iftikhar Ahmad 
  Abstract: Mobile crowdsensing (MCS) is a technique where individuals voluntarily utilise their devices to collect data to measure phenomena. In this article, a review of privacy-preserving in MCS is presented. First, it highlights MCS definitions, architecture, and unique characteristics. Then, it provides background knowledge about MCS. Afterward, a privacy-oriented MCS taxonomy in terms of privacy-oriented; data reliability, incentive, and task allocation user recruitment mechanisms, is devised. This work explores contemporary state-of-the-art issues related to privacy and security. It reviews 35 recent research published by high-quality sources and provides a topic-oriented survey for these efforts. It shows that only 16% of the papers evaluate their schemes through experiments on real smartphones, and Huawei is the most widely used mobile (45%). It shows an increasing trend in publications from 2017 till now. It highlights recent challenges faced the privacy in MCS and potential research directions for developing more advanced methods to optimise MCS
  Keywords: mobile crowdsensing; MCS; privacy preservation; data reliability; untrustworthy; incentive; user recruitment.
  DOI: 10.1504/IJSNET.2022.10048935
   
  
• Security Demonstration for the Quantum Noise-based Physical Layer Using Variable Keys  
  by Shuai Shi, Ning Xiao 
  Abstract: With the continued advancement of science and technology, a large amount of important information is carried by optical fibre networks. Therefore, it is imperative to use secure transmission strategies to protect important information. The Y-00 cipher that employs multi-order modulation to prevent eavesdropping on ciphertext is a practical candidate for providing data protection at the physical layer. The Y-00 cipher combines the mathematical encryption of multilevel signalling and quantum noise to provide high security to fibre communications. This paper proposes a quantum noise-based physical layer secure transmission scheme, combining the Y-00 cipher with time-domain spectral phase encoding (TDSPE). The operation methods of the Y-00 cipher in the data encryption and TDSPE in the key distribution are introduced. Then, the system performance is investigated by transmission experiments. The noise-masking phenomenon is demonstrated and quantified. The probability of the eavesdropper guessing cipher text correctly is evaluated. Last, the proposed secure transmission is achieved at 1 Gbps over a 100.2 km optical fibre link, with an intensity level of 1024 and a noise masking number of 71. The experimental results prove the effective feasibility and high security.
  Keywords: quantum noise-based; variable keys; physical layer; optical fibre link; Y-00 cipher; TDSPE.
  DOI: 10.1504/IJSNET.2022.10049293
   
  
• Multi-applicable text classification based on deep neural network  
  by Jingjing Yang, Feng Deng, Suhuan Lv, Rui Wang, Qi Guo, Zongchun Kou, Shiqiang Chen 
  Abstract: Most long text classification methods based on deep learning have problems such as semantics sparsity and long-distance dependence. To tackle these problems, a novel multi-applicable text classification based on deep neural network (MTDNN) is proposed, which contains a bidirectional encoder representation from transformer (BERT), a dimension reduction layer, and the bidirectional long short-term memory (Bi-LSTM) combining attention mechanism. BERT is used to pre-train the words into the word embedding vectors. The dimension reduction layer extracts the feature phrase representations with higher weight from the word embedding vectors. The Bi-LSTM captures both the forward and backward context representations. An attention mechanism is employed to focus on the information outputted from the Bi-LSTM. The experimental results illustrate that the accuracy of the MTDNN for long text, short text classification, and sentiment analysis reaches 94.95%, 93.53% and 92.32%, respectively. The results show that our method outperforms the other state-of-the-art text classification methods.
  Keywords: text classification; deep neural network; BERT; long short-term memory; LSTM; attention mechanism; multi-applicable.
  DOI: 10.1504/IJSNET.2022.10049687
   
  
• An adaptive multi-group slime mould algorithm for node localization in wireless sensor networks  
  by Xiankang He, Lijun Yan, Shi-Jian Liu, Jixiang Lv, Jeng-Shyang Pan 
  Abstract: Node localization is a common and significant practical application question in wireless sensor network (WSN). The goal of this problem is to use anchor nodes in the network to estimate the geographical location of the unknown node. A novel algorithm, named adaptive multi-group slime mould algorithm (AMSMA), is proposed in this study. The improved slime mould algorithm uses the multi-group strategy and adaptive communication mechanism to alleviate the lack of population diversity, development and exploration imbalance of the slime mould algorithm. The proposed AMSMA was tested under CEC2013 test suite. Compared with SMA and corresponding optimization algorithms, the AMSMA is more effective and efficient. In addition, a novel localization algorithm based on AMSMA is proposed. The AMSMA-Distance Vector-Hop (AMSMA-DV-Hop) is applied to the localization of WSN. Compared with some other existing localization algorithms, the proposed AMSMA-DV-Hop is an effective algorithm for the localization of WSN.
  Keywords: WSN; multi-group; adaptive; slime mould algorithm.
  
  
• A novel indoor positioning algorithm based on UWB  
  by Zhixue Tong, Junhao Xue, Zhiqiang Kang 
  Abstract: The positioning method based on ultra-wideband (UWB) technology has been widely used in real life. In order to improve the indoor positioning accuracy of UWB, a new indoor positioning algorithm is proposed. Firstly, in the aspect of non-iterative method, a weighted least squares (WLS) method is proposed to solve the problem that the traditional least squares (LS) method cannot take into account the factors affecting the positioning accuracy of the system due to the different location of the base station in the indoor positioning system. Secondly, in terms of iterative method, a closed-form Newton iterative method considering high-order terms is proposed in view of the factor that the traditional Gauss-Newton iterative method only performs Taylor first-order expansion to cause large model deviation. Finally, the experimental results show that the positioning accuracy of the two proposed algorithms has been improved to a certain extent.
  Keywords: sensor; UWB; indoor positioning; time-of-flight; ranging error.
  DOI: 10.1504/IJSNET.2022.10050067
   
  
• Hybrid solution for smart rural applications in areas without Internet coverage  
  by Luis Miguel Bartolín-Arnau, David Todoli-Ferrandis, Javier Silvestre-Blanes, Víctor M. Sempere-Payá, Salvador Santonja-Climent 
  Abstract: Agriculture and livestock farming are one of the main economic bases of rural areas in Southern Europe, with many classified as sparsely populated. This article proposes and tests a technical solution to support different applications that require a large range of coverage and tracking assets, as can be found for instance in smart farming, or environmental monitoring. LoRaWAN, which provides several kilometres of coverage, low battery consumption and robust communications, is selected as the solution to overcome the lack of cellular network connectivity in those areas. Then, the design of a hybrid solution for rural areas, that requires the use of a LoRaWAN mobile gateway to extend the range of LoRaWAN coverage, is presented. In addition, to ensure quality of service (QoS) and avoid data loss, a 5G internet connection is used to provide connectivity to the LoRaWAN gateway, combined with the store and forward communications mechanism to avoid data loss in areas without internet connection.
  Keywords: LoRaWAN; 5G; store and forward; S&F; smart agriculture; smart livestock; mobile gateway LoRaWAN.
  DOI: 10.1504/IJSNET.2022.10050654
   
  
• Superactive: A Priority, Latency, and SLA-aware Resource Management Scheme for Software Defined Space-Air-Ground Integrated Networks  
  by Mahfuzulhoq Chowdhury 
  Abstract: The software-defined space-air-ground integrated network (SAGIN) is regarded as future-generation networking solution due to its wide-area coverage and seamless communication support for ground networks and resource-intensive application support for space-air networks. The SDN-based literary works are restricted only to resource management for space-air or ground networks, not both. The resource scheduling for multi-users caching, computing and data-transfer task accomplishment over software-defined SAGIN were out of their investigations by taking proactive local SDN, heterogenous users and applications, SLA requirements, priority, latency, and users’ budget into account. To outperform the problems, this paper advocates a proactive SDN-based resource management scheme considering SLA requirements, latency, priority, and users’ budget for multi-user task completion over software-defined SAGIN. This paper contributes an analytical model that covers task completion time, energy expenditure, financial cost, and SLA fulfilment metrics. The performance results illustrate that proposed scheme produces 72% time and 69% financial gain over the compared scheme.
  Keywords: superactive resource management; software-defined networking; SDN; space-air-ground integrated networks; SAGIN; task completion time; profit; throughput.
  DOI: 10.1504/IJSNET.2022.10050822
   
  
• Artificial Intelligence in Human Activity Recognition: A Review  
  by Updesh Verma, Pratibha Tyagi, Manpreet Kaur 
  Abstract: The various activities of human movements have been discussed for several years, such as sports activities, daily life activities, and so on. Their detection and classification have been given crucial information about a person’s behaviour and health status. So, there has always been a purpose for detecting and classifying these activities for real-life problems. Behavioural recognition, fall detection, intrusion detection, human health prediction model, ambulatory monitoring, smart access to electronic appliances, etc., are the main motives of the detection of physical activity in the context of daily life. Nowadays, various types of wearable sensors are available in tiny sizes due to the advancements in miniature technology in electronic devices, which proved very useful for detecting human motions. Here in this article, some important methodologies, physical activity basics, and their classification using machine learning and deep learning approaches are discussed in the context of wearable sensors. After reading this article, the researcher could summarise the whole theory and technical aspects of activity recognition. Wearable sensors have gained tremendous space for sensing human motion due to their various advantages over other sensors.
  Keywords: wearable sensors; deep learning models; machine learning models; accelerometer; gyroscope; activity recognition.
  DOI: 10.1504/IJSNET.2022.10050953
   
  
• Improving Indoor Positioning System Using Weighted Linear Least Square and Neural Network  
  by Ngoc-Son Duong, Thanh-Phuc Nguyen, Quoc-Tuan Nguyen, Thai-Mai Dinh-Thi 
  Abstract: Indoor positioning has grasped great attention in recent years. Many of those technologies are related to the problem of determining the position of an object in space, such as the robot, people, and so on. In this paper, we combine a range-free method, i.e., fingerprinting, and a range-based method, i.e., multi-lateration, to propose a novel indoor positioning system using the received signal strength indicator (RSSI). First, we apply multi-layer perceptron neural network (MLP-NN) on a time series of RSS readings to coarsely estimate the target location. From the knowledge of the coarse location, we select reliable beacons and apply least square-based multi-lateration to their estimated distance to finely estimate the target position. We also proposed a novel weighted least square method based on uncertainty propagation to improve localisation accuracy. Experiments have shown that our proposed system, which is implemented on Raspberry Pi (RPi), is highly precise and deployable.
  Keywords: Bluetooth low energy; BLE; lateration; BLE beacon; indoor positioning; indoor localisation; fingerprinting; least square; weighted least square; neural network.
  DOI: 10.1504/IJSNET.2022.10051518
   
  
• A Snort-based Secure Edge Router for Smart Home  
  by Narottam Patel, B.M. MEHTRE, Rajeev Wankar 
  Abstract: Cybercrimes are rising rapidly with the increasing use of the internet of things (IoT)-based gadgets at home. For instance, the Mirai-BotNet infected and compromised many IoT-based devices and routers, creating a zombie network of robots that can be controlled remotely. There is a need for a cost-effective, secure router for a smart home. This paper investigates and proposes a Snort-based secure edge router for smart home (SERfSH), which is resilient to many cyberattacks. SERfSH automatically generates Snort content rules by combining the extracted string, location information, header information, and sequential pattern. The experimental setup of SERfSH consists of a Raspberry Pi 4 model, an ESP32 microcontroller, six IoT devices, and a malicious actor machine. The proposed SERfSH is tested for 15 attacks, and the results show that 14 attacks were detected and 12 attacks were mitigated.
  Keywords: intrusion detection system; IDS; Snort; IoT attacks; intrusion prevention system; IPS; cyber security.
  DOI: 10.1504/IJSNET.2022.10051521
   
  
• Analysis of GNSS data quality based on Anubis and RTKLIB and single-point positioning accuracy in different environments for Android smartphones  
  by Qiaoli Kong, Qi Bai, Tianfa Wang, Changsong Li, Yanfei Chen, Jingwei Han 
  Abstract: With the improvement of global navigation satellite system (GNSS) and the popularity of smart devices, location-based services based on smartphones have been developed rapidly, but there are few studies that compare the GNSS data quality and positioning ability of single-frequency smartphones (Huawei P10) and dual-frequency smartphones (Xiaomi 8 and Huawei Mate40Pro). This paper aims to analyse the GNSS data quality of each of the smartphones above using G/NUT-Anubis and RTKLIB software, and realise the single-point positioning of smartphones in open, obscured and indoor environments, respectively. The test results show that the GNSS data quality of smartphones is only a little worse than that of geodetic receiver, and the tracking abilities of the Xiaomi 8 and Huawei Mate40Pro are better than that of the Huawei P10. In open and obscured environments, the positioning accuracy of the Huawei Mate40Pro is at the metre level and higher than that of the Xiaomi 8.
  Keywords: Android smartphone; raw GNSS observations; data quality analysis; G/NUT-Anubis; single-point positioning; SPP; RTKLIB.
  DOI: 10.1504/IJSNET.2022.10051523