Search Results (13)

The occurrence of fires in tropical peatlands poses significant threats to their ecosystems. An Internet of Things (IoT) system was developed to measure and collect fire risk factors in the Raja Musa Forest Reserve (RMFR) in Selangor, Malaysia, to address this issue. In this paper, neural networks with different layers were employed to predict peatland forests’ Fire Weather Index (FWI). The neural network models used two sets of input parameters, consisting of four and nine fire factors. The predicted FWI values were compared with actual values obtained from the Malaysian meteorological department. The findings revealed that the five-layer neural network outperformed others in both the four-input and nine-input models. Specifically, the nine-input neural network achieved a mean square error (MSE) of 1.116 and a correlation of 0.890, surpassing the performance of the four-input neural network with the MSE of 1.537 and the correlation of 0.852. These results hold significant research and practical implications for precise peatland fire prevention, control, and the formulation of preventive measures. Full article

Peatland refers to the peat soil and wetland biological environment growing on the surface. However, unexpected fires in peatlands frequently have brought severe greenhouse gas emissions and transboundary haze to Southeast Asia. To alleviate this issue, this paper first establishes an Internet of Things (IoT) system for peatland monitoring and management in the Raja Musa Forest Reserve (RMFR) in Selangor, Malaysia, and proposes a more efficient and low-complexity model for calculating the Duff Moisture Code (DMC) in peatland forests using groundwater level (GWL) and relative humidity. The feasibility of the IoT system is verified by comparing its data with those published by Malaysian Meteorological Department (METMalaysia). The proposed Linear_DMC Model and Linear_Mixed_DMC Model are compared with the Canadian Fire Weather Index (FWI) model, and their performance is evaluated using IoT measurement data and actual values published by METMalaysia. The results show that the correlation between the measured data of the IoT system and the data from METMalaysia within the same duration is larger than 0.84, with a mean square error (MSE) of 2.56, and a correlation of 0.91 can be achieved between calculated DMC using the proposed model and actual values. This finding is of great significance for predicting peatland forest fires in the field and providing the basis for fire prevention and decision making to improve disaster prevention and reduction. Full article

Internet of Things networks (IoT) are becoming very important in industrial, medical, and commercial applications. The security aspect of IoT networks is critical, especially the authentication of the devices in the network. The current security model in IoT networks uses centralized key exchange servers that present a security weak point. IoT networks need decentralized management for network security. Blockchain, with its decentralized model of authentication, can provide a solution for decentralized authentication in IoT networks. However, blockchain authentication models are known to be computationally demanding because they require complex mathematical calculations. In this paper, we present an Authentication-Chains protocol which is a lightweight decentralized protocol for IoT authentication based on blockchain distributed ledger. The proposed protocol arranges the nodes in clusters and creates an authentication blockchain for each cluster. These cluster chains are connected by another blockchain. A new consensus algorithm based on proof of identity authentication is adapted to the limited computational capabilities of IoT devices. The proposed protocol security performance is analyzed using cryptographic protocols verifier software and tested. Additionally, a test bed consisting of a Raspberry Pi network is presented to analyze the performance of the proposed protocol. Full article

Basal stem rot (BSR) disease of oil palm (Elaeis guineensis Jacq.) spreads through the contact of the plant roots with Ganoderma boninense (G. boninense) Pat. inoculum in the soil. The soil properties can be altered by growing seedlings with or without G. boninense inoculum. In the early stage of infection, the symptoms are difficult to detect. Therefore, an understanding of the environmental soil conditions of the plant is crucial in order to indicate the presence of the fungus. This paper presents an analysis of the temporal changes of the soil properties associated with the G. boninense infection in oil palm seedlings. A total of 40 seedlings aged five months were used in the study, comprising 20 inoculated (infected seedlings: IS) and 20 control (healthy seedlings: HS) seedlings. The seedlings were grown in a greenhouse for six months (24 weeks) under a controlled environmental temperature and humidity. The data of the soil moisture content (MC in %), electrical conductivity (EC in µS/cm), and temperature (T in °C) for each seedling were collected daily using three MEC10 soil sensors every hour and then transferred to the ThingSpeak cloud using a 3G Internet connection. Based on the results, the mean MC and EC showed a decreasing trend, while the mean T showed an increasing trend in both HS and IS during the six-month monitoring period. The overall mean in both the monthly and weekly analysis of MC, EC, and T was higher in HS than IS. However, in the monthly analysis, a Student’s t-test at a 5% significance level showed that only the soil MC and EC were significantly different between HS and IS, while in the weekly analysis, HS was significantly different from IS in all parameters. This study suggests that soil MC, EC, and T can be used as indicators of the G. boninense infection, especially for the weekly data. Full article

Connected vehicles have emerged as the latest revolution in the automotive industry, utilizing the advent of the Internet of Things (IoT). However, most IoT-connected cars mechanisms currently depend on available network services and need continuous network connections to allow users to connect to their vehicles. Nevertheless, the connectivity availability shortcoming in remote or rural areas with no network coverage makes vehicle sharing or any IoT-connected device problematic and undesirable. Furthermore, IoT-connected cars are vulnerable to various passive and active attacks (e.g., replay attacks, MiTM attacks, impersonation attacks, and offline guessing attacks). Adversaries could all use these attacks to disrupt networks posing a threat to the entire automotive industry. Therefore, to overcome this issue, we propose a hybrid online and offline multi-factor authentication cross-domain authentication method for a connected car-sharing environment based on the user’s smartphone. The proposed scheme lets users book a vehicle using the online booking phase based on the secured and trusted Kerberos workflow. Furthermore, an offline authentication phase uses the OTP algorithm to authenticate registered users even if the connectivity services are unavailable. The proposed scheme uses the AES-ECC algorithm to provide secure communication and efficient key management. The formal SOV logic verification was used to demonstrate the security of the proposed scheme. Furthermore, the AVISPA tool has been used to check that the proposed scheme is secured against passive and active attacks. Compared to the previous works, the scheme requires less computation due to the lightweight cryptographic algorithms utilized. Finally, the results showed that the proposed system provides seamless, secure, and efficient authentication operation for the automotive industry, specifically car-sharing systems, making the proposed system suitable for applications in limited and intermittent network connections. Full article

Recent Society 5.0 efforts by the Government of Japan are aimed at establishing a sustainable human-centered society by combining new technologies such as sensor networks, edge computing, Internet of Things (IoT) ecosystems, artificial intelligence (AI), big data, and robotics. Many research works have been carried out with an increasing emphasis on the fundamentals of wireless sensor networks (WSN) for different applications; namely precision agriculture, environment, medical care, security, and surveillance. In the same vein, almost all of the known authentication techniques rely on the single gateway node, which is unsuitable for the current sensor nodes that are broadly distributed in the real world. Despite technological advances, resource constraints and vulnerability to an attacker physically capturing some sensor nodes have remained an important and challenging research field for developing wireless sensor network user authentication. This work proposes a new authentication scheme for agriculture professionals based on a multi-gateway communication model using a fuzzy extractor algorithm to support the Society 5.0 environment. The scheme provides a secure mutual authentication using the well-established formal method called BAN logic. The formal security verification of the proposed scheme is validated with the AVISPA tool, a powerful validation method for network security applications. In addition, the security of the scheme was informally analyzed to demonstrate that the scheme is secure from different attacks, e.g., sensor capture, replay, and other network and physical attacks. Furthermore, the communication and computation costs of the proposed scheme are evaluated and show better performance than the existing authentication schemes. Full article

Deterministic latency is an urgent demand to pursue the continuous increase in intelligence in several real-time applications, such as connected vehicles and automation industries. A time-sensitive network (TSN) is a new framework introduced to serve these applications. Several functions are defined in the TSN standard to support time-triggered (TT) requirements, such as IEEE 802.1Qbv and IEEE 802.1Qbu for traffic scheduling and preemption mechanisms, respectively. However, implementing strict timing constraints to support scheduled traffic can miss the needs of unscheduled real-time flows. Accordingly, more relaxed scheduling algorithms are required. In this paper, we introduce the flexible window-overlapping scheduling (FWOS) algorithm that optimizes the overlapping among TT windows by three different metrics: the priority of overlapping, the position of overlapping, and the overlapping ratio (OR). An analytical model for the worst-case end-to-end delay (WCD) is derived using the network calculus (NC) approach considering the relative relationships between window offsets for consecutive nodes and evaluated under a realistic vehicle use case. While guaranteeing latency deadline for TT traffic, the FWOS algorithm defines the maximum allowable OR that maximizes the bandwidth available for unscheduled transmission. Even under a non-overlapping scenario, less pessimistic latency bounds have been obtained using FWOS than the latest related works. Full article

Several wireless devices and applications can be connected through wireless communication technologies to exchange data in future intelligent health systems (e.g., the Internet of Medical Things (IoMT)). Smart healthcare requires ample bandwidth, reliable and effective communications networks, energy-efficient operations, and quality of service support (QoS). Healthcare service providers host multi-servers to ensure seamless services are provided to the end-users. By supporting a multi-server environment, healthcare medical sensors produce many data transmitted via servers, which is impossible in a single-server architecture. To ensure data security, secure online communication must be considered since the transmitted data are sensitive. Hence, the adversary may try to interrupt the transmission and drop or modify the message. Many researchers have proposed an authentication scheme to secure the data, but the schemes are vulnerable to specific attacks (modification attacks, replay attacks, server spoofing attacks, Man-in-the middle (MiTM) attacks, etc.). However, the absence of an authentication scheme that supports a multi-server security in such a comprehensive development in a distributed server is still an issue. In this paper, a secure authentication scheme using wireless medical sensor networks for a multi-server environment is proposed (Cross-SN). The scheme is implemented with a smart card, password, and user identity. Elliptic curve cryptography is utilized in the scheme, and Burrows–Abadi–Needham (BAN) logic is utilized to secure mutual authentication and to analyse the proposed scheme’s security. It offers adequate protection against replies, impersonation, and privileged insider attacks and secure communication in multi-server parties that communicate with each other. Full article

The development of the industrial Internet of Things (IIoT) promotes the integration of the cross-platform systems in fog computing, which enable users to obtain access to multiple application located in different geographical locations. Fog users at the network’s edge communicate with many fog servers in different fogs and newly joined servers that they had never contacted before. This communication complexity brings enormous security challenges and potential vulnerability to malicious threats. The attacker may replace the edge device with a fake one and authenticate it as a legitimate device. Therefore, to prevent unauthorized users from accessing fog servers, we propose a new secure and lightweight multi-factor authentication scheme for cross-platform IoT systems (SELAMAT). The proposed scheme extends the Kerberos workflow and utilizes the AES-ECC algorithm for efficient encryption keys management and secure communication between the edge nodes and fog node servers to establish secure mutual authentication. The scheme was tested for its security analysis using the formal security verification under the widely accepted AVISPA tool. We proved our scheme using Burrows Abdi Needham’s logic (BAN logic) to prove secure mutual authentication. The results show that the SELAMAT scheme provides better security, functionality, communication, and computation cost than the existing schemes. Full article

Non-orthogonal multiple access (NOMA) plays an important role in achieving high capacity for fifth-generation (5G) networks. Efficient resource allocation is vital for NOMA system performance to maximize the sum rate and energy efficiency. In this context, this paper proposes optimal solutions for user pairing and power allocation to maximize the system sum rate and energy efficiency performance. We identify the power allocation problem as a nonconvex constrained problem for energy efficiency maximization. The closed-form solutions are derived using Karush–Kuhn–Tucker (KKT) conditions for maximizing the system sum rate and the Dinkelbach (DKL) algorithm for maximizing system energy efficiency. Moreover, the Hungarian (HNG) algorithm is utilized for pairing two users with different channel condition circumstances. The results show that with 20 users, the sum rate of the proposed NOMA with optimal power allocation using KKT conditions and HNG (NOMA-PKKT-HNG) is 6.7% higher than that of NOMA with difference of convex programming (NOMA-DC). The energy efficiency with optimal power allocation using DKL and HNG (NOMA-PDKL-HNG) is 66% higher than when using NOMA-DC. Full article

Wireless sensor networks (WSNs) are often deployed in hostile environments, where an adversary can physically capture some of the sensor nodes. The adversary collects all the nodes’ important credentials and subsequently replicate the nodes, which may expose the network to a number of other security attacks, and eventually compromise the entire network. This harmful attack where a single or more nodes illegitimately claims an identity as replicas is known as the node replication attack. The problem of node replication attack can be further aggravated due to the mobile nature in WSN. In this paper, we propose an extended version of multi-level replica detection technique built on Danger Theory (DT), which utilizes a hybrid approach (centralized and distributed) to shield the mobile wireless sensor networks (MWSNs) from clone attacks. The danger theory concept depends on a multi-level of detections; first stage (highlights the danger zone (DZ) by checking the abnormal behavior of mobile nodes), second stage (battery check and random number) and third stage (inform about replica to other networks). The DT method performance is highlighted through security parameters such as false negative, energy, detection time, communication overhead and delay in detection. The proposed approach also demonstrates that the hybrid DT method is capable and successful in detecting and mitigating any malicious activities initiated by the replica. Nowadays, crimes are vastly increasing and it is crucial to modify the systems accordingly. Indeed, it is understood that the communication needs to be secured by keen observation at each level of detection. The simulation results show that the proposed approach overcomes the weaknesses of the previous and existing centralized and distributed approaches and enhances the performance of MWSN in terms of communication and memory overhead. Full article

This paper investigated the throughput performance of a secondary user (SU) for a random primary user (PU) activity in a realistic experimental model. This paper proposed a sensing and frame duration of the SU to maximize the SU throughput under the collision probability constraint. The throughput of the SU and the probability of collisions depend on the pattern of PU activities. The pattern of PU activity was obtained and modelled from the experimental data that measure the wireless local area network (WLAN) environment. The WLAN signal has detected the transmission opportunity length (TOL) which was analyzed and clustered into large and small durations in the CTOL model. The performance of the SU is then analyzed and compared with static and dynamic PU models. The results showed that the SU throughput in the CTOL model was higher than the static and dynamic models by almost 45% and 12.2% respectively. Furthermore, the probability of collisions in the network and the SU throughput were influenced by the value of the minimum contention window and the maximum back-off stage. The simulation results revealed that the higher contention window had worsened the SU throughput even though the channel has a higher number of TOLs. Full article

The passive bistatic radar (PBR) system can utilize the illuminator of opportunity to enhance radar capability. By utilizing the forward scattering technique and procedure into the specific mode of PBR can provide an improvement in target detection and classification. The system is known as passive Forward Scattering Radar (FSR). The passive FSR system can exploit the peculiar advantage of the enhancement in forward scatter radar cross section (FSRCS) for target detection. Thus, the aim of this paper is to show the feasibility of passive FSR for moving target detection and classification by experimental analysis and results. The signal source is coming from the latest technology of 4G Long-Term Evolution (LTE) base station. A detailed explanation on the passive FSR receiver circuit, the detection scheme and the classification algorithm are given. In addition, the proposed passive FSR circuit employs the self-mixing technique at the receiver; hence the synchronization signal from the transmitter is not required. The experimental results confirm the passive FSR system’s capability for ground target detection and classification. Furthermore, this paper illustrates the first classification result in the passive FSR system. The great potential in the passive FSR system provides a new research area in passive radar that can be used for diverse remote monitoring applications. Full article