A review on recent studies utilizing artificial intelligence methods for solving routing challenges in wireless sensor networks.

Wireless sensor networks (WSNs) are becoming increasingly important, providing pervasive real-time applications that have been used to enhance smart environments in various fields such as smart cities, manufacturing, and the Internet of Things (IoT). This survey reviews and analyzes the research trends related to the utilized Artificial Intelligence (AI) methods for WSN and the potential enhancement of WSNs using these methods. We highlight the routing challenge in WSN and present a comprehensive discussion on the recent studies that utilized various AI methods in addressing the routing challenge to meet specific objectives of WSN, during the span of 2010 to 2020. This would guide the reader towards an understanding of up-to-date applications of AI methods with respect to routing challenge in WSN. In addition, a general evaluation is provided along with a comparison of utilized AI methods in WSNs, which guides the reader in identifying the most appropriate AI methods that can be utilized for solving the routing challenge. Finally, we conclude the paper by stating the open research issues and new directions for future research.

Determinants of tyrosinaemia during nitisinone therapy in alkaptonuria.

Nitisinone (NIT) produces inevitable but varying degree of tyrosinaemia. However, the understanding of the dynamic adaptive relationships within the tyrosine catabolic pathway has not been investigated fully. The objective of the study was to assess the contribution of protein intake, serum NIT (sNIT) and tyrosine pathway metabolites to nitisinone-induced tyrosinaemia in alkaptonuria (AKU). Samples of serum and 24-h urine collected during SONIA 2 (Suitability Of Nitisinone In Alkaptonuria 2) at months 3 (V2), 12 (V3), 24 (V4), 36 (V5) and 48 (V6) were included in these analyses. Homogentisic acid (HGA), tyrosine (TYR), phenylalanine (PHE), hydroxyphenylpyruvate (HPPA), hydroxyphenyllactate (HPLA) and sNIT were analysed at all time-points in serum and urine. Total body water (TBW) metabolites were derived using 60% body weight. 24-h urine and TBW metabolites were summed to obtain combined values. All statistical analyses were post-hoc. 307 serum and 24-h urine sampling points were analysed. Serum TYR from V2 to V6, ranging from 478 to 1983 µmol/L were stratified (number of sampling points in brackets) into groups < 701 (47), 701-900 (105), 901-1100 (96) and > 1100 (59) µmol/L. The majority of sampling points had values greater than 900 µmol/L. sPHE increased with increasing sTYR (p < 0.001). Tyrosine, HPPA and HPLA in serum and TBW all increased with rising sTYR (p < 0.001), while HPLA/TYR ratio decreased (p < 0.0001). During NIT therapy, adaptive response to minimise TYR formation was demonstrated. Decreased conversion of HPPA to HPLA, relative to TYR, seems to be most influential in determining the degree of tyrosinaemia.

Deterministic clustering based compressive sensing scheme for fog-supported heterogeneous wireless sensor networks.

Data acquisition problem in large-scale distributed Wireless Sensor Networks (WSNs) is one of the main issues that hinder the evolution of Internet of Things (IoT) technology. Recently, combination of Compressive Sensing (CS) and routing protocols has attracted much attention. An open question in this approach is how to integrate these techniques effectively for specific tasks. In this paper, we introduce an effective deterministic clustering based CS scheme (DCCS) for fog-supported heterogeneous WSNs to handle the data acquisition problem. DCCS employs the concept of fog computing, reduces total overhead and computational cost needed to self-organize sensor network by using a simple approach, and then uses CS at each sensor node to minimize the overall energy expenditure and prolong the IoT network lifetime. Additionally, the proposed scheme includes an effective algorithm for CS reconstruction called Random Selection Matching Pursuit (RSMP) to enhance the recovery process at the base station (BS) side with a complete scenario using CS. RSMP adds random selection process during the forward step to give opportunity for more columns to be selected as an estimated solution in each iteration. The results of simulation prove that the proposed technique succeeds to minimize the overall network power expenditure, prolong the network lifetime and provide better performance in CS data reconstruction.

Compressive sensing based secure data aggregation scheme for IoT based WSN applications.

Compressive Sensing (CS) based data collection schemes are found to be effective in enhancing the data collection performance and lifetime of IoT based WSNs. However, they face major challenges related to key distribution and adversary attacks in hostile and complex network deployments. As a result, such schemes cannot effectively ensure the security of data. Towards the goal of providing high security and efficiency in data collection performance of IoT based WSNs, we propose a new security scheme that amalgamates the advantages of CS and Elliptic Curve Cryptography (ECC). We present an efficient algorithms to enhance the security and efficiency of CS based data collection in IoT-based WSNs. The proposed scheme operates in five main phases, namely Key Generation, CS-Key Exchange, Data Compression with CS Encryption, Data Aggregation and Encryption with ECC algorithm, and CS Key Re-generation. It considers the benefits of ECC as public key algorithm and CS as encryption and compression method to provide security as well as energy efficiency for cluster based WSNs. Also, it solves the CS- Encryption key distribution problem by introducing a new key sharing method that enables secure exchange of pseudo-random key between the BS and the nodes in a simple way. In addition, a new method is introduced to safeguard the CS scheme from potential security attacks. The efficiency of our proposed technique in terms of security, energy consumption and network lifetime is proved through simulation analysis.

Anti-oxidant and Anti-Inflammatory Cyclic Diarylheptanoids from Alnus japonica Stem Bark.

A new cyclic diarylheptanoid namely alnuheptanoid B (3), along with four known cyclic diarylheptanoids: myricanone (1), (+)-S-myricanol (2), myricanone 5-O- -D-glucopyranoside (4), and (+)-S-myricanol 5-O- -D-glucopyranoside (5) were isolated from the EtOAc fraction of Alnus japonica Steud (family: Betulaceae) stem bark. Their structures were established by different spectroscopic analyses, as well as optical rotation measurement. Compounds 1, 2, 4, and 5 are isolated for the first time from A. japonica. The antioxidant and anti-inflammatory activities of compounds (1-5) were assessed using DPPH assay and carrageenin induced rat paw edema model, respectively. They displayed significant antioxidant activity in relation to propyl gallate (standard antioxidant) at concentration 50 µM. Compound 2 demonstrated anti-inflammatory effect at a dose 10 mg/kg compared with indomethacin (positive control).