DGTTSSA: Data Gathering Technique Based on Trust and Sparrow Search Algorithm for WSNs.

Wireless Sensor Networks (WSNs) have been successfully utilized for developing various collaborative and intelligent applications that can provide comfortable and smart-economic life. This is because the majority of applications that employ WSNs for data sensing and monitoring purposes are in open practical environments, where security is often the first priority. In particular, the security and efficacy of WSNs are universal and inevitable issues. One of the most effective methods for increasing the lifetime of WSNs is clustering. In cluster-based WSNs, Cluster Heads (CHs) play a critical role; however, if the CHs are compromised, the gathered data loses its trustworthiness. Hence, trust-aware clustering techniques are crucial in a WSN to improve node-to-node communication as well as to enhance network security. In this work, a trust-enabled data-gathering technique based on the Sparrow Search Algorithm (SSA) for WSN-based applications, called DGTTSSA, is introduced. In DGTTSSA, the swarm-based SSA optimization algorithm is modified and adapted to develop a trust-aware CH selection method. A fitness function is created based on the nodes' remaining energy and trust values in order to choose more efficient and trustworthy CHs. Moreover, predefined energy and trust threshold values are taken into account and are dynamically adjusted to accommodate the changes in the network. The proposed DGTTSSA and the state-of-the-art algorithms are evaluated in terms of the Stability and Instability Period, Reliability, CHs Average Trust Value, Average Residual Energy, and Network Lifetime. The simulation results indicate that DGTTSSA selects the most trustworthy nodes as CHs and offers a significantly longer network lifetime than previous efforts in the literature. Moreover, DGTTSSA improves the instability period compared to LEACH-TM, ETCHS, eeTMFGA, and E-LEACH up to 90%, 80%, 79%, 92%, respectively, when BS is located at the center, up to 84%, 71%, 47%, 73%, respectively, when BS is located at the corner, and up to 81%, 58%, 39%, 25%, respectively, when BS is located outside the network.

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.

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.

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.

GWRA: grey wolf based reconstruction algorithm for compressive sensing signals.

The recent advances in compressive sensing (CS) based solutions make it a promising technique for signal acquisition, image processing and other types of data compression needs. In CS, the most challenging problem is to design an accurate and efficient algorithm for reconstructing the original data. Greedy-based reconstruction algorithms proved themselves as a good solution to this problem because of their fast implementation and low complex computations. In this paper, we propose a new optimization algorithm called grey wolf reconstruction algorithm (GWRA). GWRA is inspired from the benefits of integrating both the reversible greedy algorithm and the grey wolf optimizer algorithm. The effectiveness of GWRA technique is demonstrated and validated through rigorous simulations. The simulation results show that GWRA significantly exceeds the greedy-based reconstruction algorithms such as sum product, orthogonal matching pursuit, compressive sampling matching pursuit and filtered back projection and swarm based techniques such as BA and PSO in terms of reducing the reconstruction error, the mean absolute percentage error and the average normalized mean squared error.