This paper presents an effective solution based on speech recognition to provide elderly people, patients and disabled people with an easy control system. The goal is to build a low-cost system based on speech recognition to easily access Internet of Things (IoT) devices installed in smart homes and hospitals without relying on a centralized supervisory system. The proposed system used a Raspberry Pi board to control home appliances through wireless with smartphones. The main purpose of this system is to facilitate interactions between the user and home appliances through IoT communications based on speech commands. The proposed framework contribution uses a hybrid Support Vector Machine (SVM) with a Dynamic Time Warping (DTW) algorithm to enhance the speech recognition process. The proposed solution is a machine learning-based system for controlling smart devices through speech commands with an accuracy of 97%. The results helped patients and elderly people to access and control IoT devices that are compatible with our system using speech recognition. The proposed speech recognition system is flexible with scalability and availability in adapting to existing smart IoT devices, and it provides privacy in managing patient devices. The research provides an effective method to integrate our systems among medical institutions to help elderly people and patients. Author Contributions: Conceptualization, A.I.; Formal analysis, S.A.; Project administration, I.M.E.; Software, A.I. All authors have read and agreed to the published version of the manuscript. Conflicts of Interest:The authors declare that there is no conflict of interest.

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Smart manufacturing is a manufacturing strategy that is principally based on the digitization of manufacturing related activities and the rapid conversion of data into information. Innovations in big data analysis can be used to support the quick data-driven decision making processes needed for today's turbulent markets [1-3]. Big data refers to the large volumes of structured, semi-structured, and unstructured data, acquired from a variety of heterogeneous sources [4]. This data is typically assumed to have the valuable information hidden in it because substantial efforts and resources are needed to uncover it [5, 6]. According to the U.S. National Institute of Science and Technology (NIST) Big Data Public Working Group (Reference Architecture Subgroup) [7], big data does not refer to the increasingly large datasets or the requirement for improved performance and efficiency. Instead, it refers to the fundamental reforms in the architecture needed to manage this data [1-3]. Big data analytics are currently used for many industrial applications. This includes product lifecycle management [8], process redesign [9], supply chain management [10], and production systems data analysis [11]. Of these, production systems analysis has

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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.