Deploying large numbers of mobile robots which can interact with each other produces swarm intelligent behavior. However, mobile robots are normally running with finite energy resource, supplied from finite battery. The limitation of energy resource required human intervention for recharging the batteries. The sharing information among the mobile robots would be one of the potentials to overcome the limitation on previously recharging system. A new approach is proposed based on integrated intelligent system inspired by foraging of honeybees applied to multimobile robot scenario. This integrated approach caters for both working and foraging stages for known/unknown power station locations. Swarm mobile robot inspired by honeybee is simulated to explore and identify the power station for battery recharging. The mobile robots will share the location information of the power station with each other. The result showed that mobile robots consume less energy and less time when they are cooperating with each other for foraging process. The optimizing of foraging behavior would result in the mobile robots spending more time to do real work.
Cardiovascular Disease (CVD) is a primary cause of heart problems such as angina and myocardial ischemia. The detection of the stage of CVD is vital for the prevention of medical complications related to the heart, as they can lead to heart muscle death (known as myocardial infarction). The electrocardiogram (ECG) reflects these cardiac condition changes as electrical signals. However, an accurate interpretation of these waveforms still calls for the expertise of an experienced cardiologist. Several algorithms have been developed to overcome issues in this area. In this study, a new scheme for myocardial ischemia detection with multi-lead long-interval ECG is proposed. This scheme involves an observation of the changes in ischemic-related ECG components (ST segment and PR segment) by way of the Choi-Williams time-frequency distribution to extract ST and PR features. These extracted features are mapped to a multi-class SVM classifier for training in the detection of unknown conditions to determine if they are normal or ischemic. The use of multi-lead ECG for classification and 1 min intervals instead of beats or frames contributes to improved detection performance. The classification process uses the data of 92 normal and 266 patients from four different databases. The proposed scheme delivered an overall result with 99.09% accuracy, 99.49% sensitivity, and 98.44% specificity. The high degree of classification accuracy for the different and unknown data sources used in this study reflects the flexibility, validity, and reliability of this proposed scheme. Additionally, this scheme can assist cardiologists in detecting signal abnormality with robustness and precision, and can even be used for home screening systems to provide rapid evaluation in emergency cases.
Channel rendezvous is an initial and important process for establishing communications between secondary users (SUs) in distributed cognitive radio networks. Due to the drawbacks of the common control channel (CCC) based rendezvous approach, channel hopping (CH) has attracted a lot of research interests for achieving blind rendezvous. To ensure rendezvous within a finite time, most of the existing CH-based rendezvous schemes generate their CH sequences based on the whole global channel set in the network. However, due to the spatial and temporal variations in channel availabilities as well as the limitation of SUs sensing capabilities, the local available channel set (ACS) for each SU is usually a small subset of the global set. Therefore, following these global-based generated CH sequences can result in extensively long time-to-rendezvous (TTR) especially when the number of unavailable channels is large. In this paper, we propose two matrix-based CH rendezvous schemes in which the CH sequences are generated based on the ACSs only. We prove the guaranteed and full diversity rendezvous of the proposed schemes by deriving the theoretical upper bounds of their maximum TTRs. Furthermore, extensive simulation comparisons with other existing works are conducted which illustrate the superior performance of our schemes in terms of the TTR metrics.
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.
In heterogeneous wireless networks, the industrial Internet of Things (IIoT) is an essential contributor to increasing productivity and effectiveness. However, in various domains, such as industrial wireless scenarios, small cell domains, and vehicular ad hoc networks, an efficient and stable authentication algorithm is required (VANET). Specifically, IoT vehicles deal with vast amounts of data transmitted between VANET entities in different domains in such a large-scale environment. Also, crossing from one territory to another may have the connectivity services down for a while, leading to service interruption because it is pervasive in remote areas and places with multipath obstructions. Hence, it is vulnerable to specific attacks (e.g., replay attacks, modification attacks, man-in-the-middle attacks, and insider attacks), making the system inefficient. Also, high processing data increases the computation and communication cost, leading to an increased workload in the system. Thus, to solve the above issues, we propose an online/offline lightweight authentication scheme for the VANET cross-domain system in IIoT to improve the security and efficiency of the VANET. The proposed scheme utilizes an efficient AES-RSA algorithm to achieve integrity and confidentiality of the message. The offline joining is added to avoid remote network intrusions and the risk of network service interruptions. The proposed work includes two different significant goals to achieve first, then secure message on which the data is transmitted and efficiency in a cryptographic manner. The Burrows Abdi Needham (BAN logic) logic is used to prove that this scheme is mutually authenticated. The system's security has been tested using the well-known AVISPA tool to evaluate and verify its security formally. The results show that the proposed scheme outperforms the ID-CPPA, AAAS, and HCDA schemes by 53%, 55%, and 47% respectively in terms of computation cost, and 65%, 83%, and 40% respectively in terms of communication cost.
The non-stationary and multi-frequency nature of biomedical signal activities makes the use of time-frequency distributions (TFDs) for analysis inevitable. Time-frequency analysis provides simultaneous interpretations in both time and frequency domain enabling comprehensive explanation, presentation and interpretation of electrocardiogram (ECG) signals. The diversity of TFDs and specific properties for each type show the need to determine the best TFD for ECG analysis. In this study, a performance evaluation of five TFDs in term of ECG abnormality detection is presented. The detection criteria based on extracted features from most important ECG signal components (QRS) to detect normal and abnormal cases. This is achieved by estimating its energy concentration magnitude using the TFDs. The TFDs analyse ECG signals in one-minute interval instead of conventional time domain approach that analyses based on beat or frame containing several beats. The MIT-BIH normal sinus rhythm ECG database total records of 18 long-term ECG sampled at 128 Hz have been analysed. The tested TFDs include Dual-Tree Wavelet Transform, Spectrogram, Pseudo Wigner-Ville, Choi-Williams, and Born-Jordan. Each record is divided into one-minute slots, which is not considered previously, and analysed. The sample periods (slots) are randomly selected ten minutes interval for each record. This result with 99.44% detection accuracy for 15,735 ECG beats shows that Choi-Williams distribution is most reliable to be used for heart problem detection especially in automated systems that provide continuous monitoring for long time duration.
In order to implement the fifth generation (5G) communication system for a large number of users, the governments of many countries nominated the low 5G frequency band between 3.3 and 4.3 GHz. This paper proposes a wideband RFPA by designing the input matching network (MN) and output MN of the device using the simplified real frequency technique (SRFT) and the harmonic tuning network. The load-pull and source-pull is applied at multiple points for 100 MHz intervals over the bandwidth to obtain the optimum impedances at the output and input of the 10W Gallium Nitride (GaN) Cree CGH40010F device. To verify the design, the RFPA is simulated, and the performance is measured between 3.3 and 4.3 GHz. According to experimental findings, the measured drain efficiency (DE) throughout the whole bandwidth ranged from 57.5 to 67.5% at the output power of 40 dBm. Moreover, at the 1 dB compression point between 39.2 and 42.2 dBm output power, the drain efficiency (DE) achieves a high value of 81.2% with an output power of 42.2 dBm at a frequency of 3.3 GHz. The RFPA can obtain a maximum gain of 12.4 dB at 3.5 GHz. The linearity of the RFPA with a two-tone signal is measured and the value is less than -22 dBc all over the band.