International Journal of Sensors Wireless Communications and Control - Volume 11, Issue 9, 2021

Information Retrieval (IR) is a field that concerns the structure, memory, analysis, and access to pieces of information. It has a wide application in various areas like search engines, communication systems, information filtering, medical search, etc., and helps design efficient and secure applications. This area has been a surge of research from the last few years due to data mining's unparalleled success, deep learning in computer vision, blockchain technology, etc. Core models, performance evaluation techniques, IR system applications, and its role in blockchain technology have been proposed in this literature, calling the need for a broad survey to focus the research in this promising area. This paper fills the space by surveying the state of art approaches with deep learning models, query expansion techniques used, and the use of private information retrieval in blockchain technology. This survey paper includes different IR models like boolean model, vector space model, probabilistic model, language model, N-gram model, fuzzy model, Latent Semantic Indexing (LSI) Model, Bayesian network, Evolutionary algorithm-based models and Machine Learning based models. Applications of IR systems along with different datasets are also included to provide further research in this field.

Background: Low-Density Parity-Check (LDPC) codes have received significant interest in a variety of communication systems due to their superior performance and reasonable decoding complexity. Methods: A novel Collection of Punctured Codes Decoding (CPCD) technique that considers a code as a collection of its punctured codes is proposed. Two forms of CPCD, serial CPCD that decodes each punctured code serially and parallel CPCD that decodes each punctured code in parallel, are discussed. In contrast to other modifications of LDPC decoding documented in the literature, the proposed CPCD technique views a LDPC code as a collection of punctured LDPC codes, where all punctured codes are derived from the original LDPC code by removing different portions of its parity bits. CPCD technique decodes each punctured code separately and exchanges extrinsic information obtained from that decoding among all other punctured codes for their decoding. Hence, as the iterations increase, the information obtained in the decoding of punctured codes improve making CPCD perform better than standard decoding. Results: It is demonstrated that both serial and parallel CPCD have about the same decoding complexity compared with standard Sum Product Algorithm (SPA) decoding. It is also demonstrated that while serial CPCD has about the same decoding delay compared with standard SPA decoding, parallel CPCD can decrease the decoding delay, however, at the expense of processing power. Conclusion: Numerical results demonstrate that CPCD can either significantly improve the performance, or significantly increase the code rate of LDPC codes.

Background: The number of elderly people is growing, and it becomes a challenge for families to safeguard their health. They are facing decline in their health and missing some of the quality of life provided to them. They are unable to control their self. They are vulnerable to fall and get in dangerous or restricted area. Aim: This study aims towards an experiment using Real-Time Location System (RTLS) via ZigBee technology in order to track the elderly person at his/her home. Objectives: To track the old people and provide them proper care and support whenever they are facing challenging times. Methods: RTLS is a system that offers a real-time tracking and positioning of people or objects. Results: The results showed that the ZigBee technology provides valuable insights to find the location of an elderly person at home and sends an alert when a person gets in a restricted area. Conclusion: ZigBee has contributed to be one of the best of its kind of protocol that can help to track and manage aged people at their house.

In this paper, we have proposed different deployment strategies and have applied area-wise clustering along with modified Ant Colony Optimization to minimize energy consumption. Background: Previously, some deployment strategies were used to enhance the lifetime of WSN. In our research, we have applied some novel deployment strategies like random, spiral, and S-pattern along with a novel area-wise clustering process to get better results than the existing literature, as shown in Table 4. Objective: The main objective of the research article is to enhance the lifetime of Wireless Sensor Network with the help of different deployment strategies like random, spiral, and S-pattern). A novel clustering process (i.e., area-wise clustering), and a Meta-heuristic algorithm (modified ACO) are applied. Method: We have applied different methods for deployment strategies (random, spiral, and S-pattern). A novel clustering process (i.e., area-wise clustering), and a Meta-heuristic algorithm (modified ACO) are applied to get the desired results. Results: Random Deployment: 11.15 days to 15.09 days. Spiral Deployment: 11.25 days to 15.23 days. S-Pattern Deployment: 11.33 days to 15.33 days. Conclusion: In this paper, efficient Wireless Sensor Networks have been configured considering energy minimization as the prime concern. To minimize the energy consumption, a modified ACO algorithm has been proposed. In our work, the minimization of energy consumption leads to an increment of the lifetime of WSN to a significant margin theoretically. The obtained result has been compared with the existing literature and it has been found that the proposed algorithm produced a better result than the existing literature.

Aim: To design and evaluate the performance of a Grey Wolf Optimization (GWO) based wrapper feature selection applied to the Botnet malware detection system. Background: A botnet is malicious software controlled by a master and used to compromise a distributed set of systems, in turn targeting a victim. Powerful attacks like Distributed Denial of Service (DDoS) can be triggered using a botnet. With the rapid growth of the Internet of Things (IoT) and its omnipresence, the vulnerable IoT devices are also under threat of being a victim or a zombie. Objective: To optimize the listed botnet data traffic features, Grey Wolf Optimization (GWO), in a wrapper model, is used to search the useful features without affecting the classification accuracy. Method: The Botnet dataset consists of a total of 192 Command and Control (C& C) botnet channels HTTP traffic features, and network traffic session-based features. GWO optimization algorithm is used as a wrapper for feature selection, and evaluated on three different classifiers, viz., SVM, KNN, and DT. Results: Decision Tree (DT) and GWO wrapper produced the best results when compared with other classifiers. The output of the research reduces the botnet traffic features to 19 from 192, with an accuracy of 99.73% post the reduction. Conclusion: The proposed DT-GWO wrapper turns out to be an excellent choice for feature reduction for botnet attack detection. The strength of the DT-GWO wrapper is that it is able to retain the near fullfeature accuracy even after a massive reduction of 90.10% of the features.

Background: In cognitive radio networks, spectrum sensing plays an important role in identifying the underutilized spectrum bands. Conventional spectrum sensing by the energy detection method uses a single detection threshold, which degrades the detection performance. Methods: Therefore double detection threshold has been proposed for spectrum sensing in the literature to improve the detection performance, but the performance depends on the region between two thresholds, termed as confusion state. Hence to improve the overall detection performance new resensing scheme has been proposed in this paper by varying the difference between thresholds by an improvement factor K. Results: The proposed method improves the detection performance compared to the single threshold method and double threshold method. Conclusion: Simulation results show that the proposed method operates better than the single threshold energy detection method and improves the detection performance at low signal-to-noise ratios.

The advanced capabilities of smart mobile devices for mobile network users have led to an explosive increase in the loads of networks, mainly driven by the increasing demand for multimedia services. Content caching in users’ devices can potentially enhance both the quality of service for mobile users and network performance. Upon users’ requests and their mobility models, a network can perform proactive caching to reduce network congestion. Furthermore, implementing an optimal caching method can increase the offloading probability, leading to an overall enhanced performance of the network. In this paper, we present an enhanced energy-based content caching and delivery approach for D2D assisted wireless networks. A cost problem is formulated for optimal caching by considering two important constraints establishing D2D links: Signal-to-Noise Ratio (SNR) and Energy Consumption (EC). An effective optimization algorithm, the Frequency Searching Adaptive Bat Algorithm (FSABA), is used to enhance the cached and delivered content, minimizing the total network cost. The system performance is analyzed and evaluated in terms of total network gain, offloading probability and average transmission delay. To validate the proposed enhancement caching approach, extended simulations are provided.

Aims: This study aims to propose an 8-bit differential input low-power Successi,e Approximation Register (SAR) ADC with digital error correction technique for sensing bio-potential signals in wearable and implantable devices. Background: As dynamic comparators have the advantages of full swing output, low power consumption, high speed, and high impedance at the input, they are preferably used in energy-efficient SAR ADC’s. However, since the dynamic comparator is the most frequently used block in SAR ADC, research is ongoing to reduce its μW power further. Also, as the offset voltage of the comparator affects the linearity of ADC, it must be minimized. Linearity can further be improved by calibrating the output of ADC, and extensive surveys on the calibration methods prove that the addition- only digital error correction method is efficient in terms of power. Objective: The objective of this study is to design a low-power and low-offset dynamic comparator intended for SAR ADC to achieve highly linear digital output. In addition to this, the objective is to implement a power-efficient digital error correction technique for the output of SAR ADC to overcome the non-idealities due to process variations. Methods: As power consumption is proportional to the number of transistors, the proposed comparator is a design obtaining the same output as the existing dynamic comparators with reduced transistor count. The proposed comparator, along with low power full swing three-input XOR logic gate, is implemented in SAR ADC with digital error correction technique in cadence 45 nm technology files, and its performance parameters are simulated. Results: The layout of the proposed dynamic comparator occupies an area of 3 μm². The simulation results of this comparator with a load of 1 pF show that it has a total offset of 11.2 mV, a delay of 0.9 ns, and power consumption of 24 nW. It also achieves a gain of 49.5, i.e., 33.86 dB. The 8-bit ADC, along with the digital error correction technique operating at 143-kS/s and under 0.6 V supply voltage simulated in 45nm technology, consumes only 0.12 μW of power. The DNL and INL errors obtained are +0.22/-0.2 LSB and -0.28 LSB, respectively. SNR limited by noise is 48.25 dB, SFDR is 48.64 dB, and ENOB achieved is 7.72. Conclusion: To satisfy the requirement of the wearable and implantable devices, a low-power SAR ADC with good linearity is designed using a low-power and low-offset dynamic comparator. A digital error correction technique using a low-power XOR logic gate is implemented at the SAR ADC output to minimize the non-idealities due to the process variations.