Abstract-Random Packet CDMA, a novel packet-based multiple access scheme for connectionless, uncoordinated random channel access is proposed. Random Packet CDMA, or RP-CDMA, utilizes a novel packet format which consists of a short header and a data portion. Each header is spread with a unique spreading code which is identical for all users and packets, while the data portion of each packet is spread by a randomly chosen spreading sequence. The receiver operates in two stages: header detection and data detection. For header detection a conventional spread spectrum receiver is sufficient. Headers are spread with a large enough processing gain to allow detection even in severe interference. The data portion is decoded with a sophisticated receiver, such as a multiuser detector, which allows for successful decoding of overlapping active packets. It is shown that the RP-CDMA system is detector capability limited and that it can significantly outperform Spread ALOHA systems whose performance is limited by the channel collision mechanism. RP-CDMA also experiences a much smaller packet retransmission rate than conventional or Spread ALOHA, and provides better spectral efficiencies.Index Terms-Random Packet Networks, RP-CDMA, ALOHA, Spread ALOHA, CDMA, multiuser detection.

A new packet-based, multiple access scheme for connectionless, uncoordinated random access is proposed using code-division multiple access (CDMA) as the physical access method. The new method uses a novel packet format with a common header with identical spreading codes for all users and packets, and random spreading codes for the data portion. The receiver operates in two stages: header detection using a conventional spread spectrum receiver and data detection using a multiuser detector to allow for decoding of overlapping active packets. The headers are spread with a large enough processing gain to allow detection even in severe interference.It is shown that this system is detector capability limited and that it can significantly outperform conventional ALOHA systems whose performance is limited by the collision mechanism. This system also experiences a much smaller packet retransmission rate and better spectral efficiency than conventional or spread ALOHA.

Industry 4.0 makes manufacturers more vulnerable to current challenges and makes it easier to adapt to market changes. This will increase the speed of innovation, make it more customer-oriented and lead to faster design processes. It is essential to focus on monitoring and controlling the production system before complex accidents occur. Moreover, an industrial control system facing information security problems in recent times because of the nature of IoT which affects the evaluation of abnormal predication. To overcome above research gaps, we shift to industrial 4.0 which combine IoT and mechanism learning for industrial monitor and manage. We propose a hybrid machine learning technique for IoT enabled industrial monitoring and control system (IoT-HML). Here, we concentrate both information security issues with accurate monitoring and control system. The first section of proposed IoT-HML system is to introduce the cat induced wheel optimization (IWO) algorithm for cluster formation. The process consists of clustering and cluster head (CH) selection. The source node forward information to destination through CH only which avoids the unwanted data loss and improve the security, because the information travel through trusted path. For route selection process, we utilize the cuckoo search algorithm to compute the optimal best path among multiples. In second section, we illustrate a coach and player learned neural network (CP-LNN) for monitoring the industrial and prevent from accidents by basic control strategies. Finally, the proposed IoT-HML system can evaluate with different set of data's to prove the effectiveness.

The Internet of Things (IoT) is a dispersed network system that connects the world through the Internet. The architecture of IoT consists of more gateways and resources which cannot be allocated in a manual process. The allocation of resources in IoT is a challenging process due to the higher consumption of energy and high latency rate. To overcome the challenges in existing works, this research introduced an Improved Reptile Search Algorithm (IRSA) to solve the optimization problem which occurs during the time of allocation resources among IoT networks. IRSA employs the methodology of levy flight and cross-over to update the candidate position and enhance the search speed in a single iteration. The proposed method consumes less energy and has low latency during data transmission from User equipment (UE) to the base station.IRSA has been compared with the existing Scalable Resource Allocation Framework (SRAF) and Improved Chaotic Firefly Algorithm (ICFA). The obtained experimental results show that the proposed IRSA attained better performance with an allocation rate of 96.40% which is comparatively higher than SRAF and ICFA with 92.40% and 91.67% respectively.

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