One of the current challenges in providing high bitrate services in next generation mobile networks is limitation of available resources. The goal of proposing a self-optimization model is to maximize the network efficiency and increase the quality of services provided to femto-cell users, considering the limited resources in radio access networks. The basis for our proposed scheme is to introduce a self-optimization model based on neighbouring relations. Using this model, we can create the possibility of controlling resources and neighbouring parameters without the need of human manipulation and only based on the network’s intelligence. To increase the model efficiency, we applied the big data technique for analyzing data and increasing the accuracy of the decision-making process in a way that on the uplink, the sent data by users is to be analyzed in self-optimization engine. The experimental results show that despite the tremendous volume of the analyzed data – which is hundreds of times bigger than usual methods – it is possible to improve the KPIs, such as throughput, up to 30 percent by optimal resource allocation and reducing the signaling load. Also, the presence of feature extraction and parameter selection modules will reduce the response time of the self-optimization model up to 25 percent when the number of parameters is too high Moreover, numerical results indicate the superiority of using support vector machine (SVM) learning algorithm. It improves the accuracy level of decision making based on the rule-based expert system. Finally, uplink quality improvement and 15-percent increment of the coverage area under satisfied SINR conditions can be considered as outcome of the proposed scheme. Manuscript profile

Network connectivity is one of the major design issues in the context of mobile sensor networks. Due to diverse communication patterns, some nodes lying in high-traffic zones may consume more energy and eventually die out resulting in network partitioning. This phenomenon may deprive a large number of alive nodes of sending their important time critical data to the sink. The application of data caching in mobile sensor networks is exponentially increasing as a high-speed data storage layer. This paper presents a deep learning-based beamforming approach to find the optimal transmission strategies for cache-enabled backhaul networks. In the proposed scheme, the sensor nodes in isolated partitions work together to form a directional beam which significantly increases their overall communication range to reach out a distant relay node connected to the main part of the network. The proposed methodology of cooperative beamforming-based partition connectivity works efficiently if an isolated cluster gets partitioned with a favorably large number of nodes. We also present a new cross-layer method for link cost that makes a balance between the energy used by the relay. By directly adding the accessible auxiliary nodes to the set of routing links, the algorithm chooses paths which provide maximum dynamic beamforming usage for the intermediate nodes. The proposed approach is then evaluated through simulation results. The simulation results show that the proposed mechanism achieves up to 30% energy consumption reduction through beamforming as partition healing in addition to guarantee user throughput. Manuscript profile

کنترل محدوده‌ها، اماکن و سنسورهای حرکتی در اینترنت اشیا نیازمند کنترل پیوسته و مستمر برای تشخیص فعالیت‌های انسانی در شرایط مختلف است که این مهم، خود چالشی از جمله نیروی انسانی و خطای انسانی را نیز در بر دارد. کنترل همیشگی توسط انسان نیز بر سنسورهای حرکتی اینترنت اشیا غیر ممکن به نظر می‌رسد. اینترنت اشیا فراتر از برقراری یک ارتباط ساده بین دستگاه‌ها و سیستم‌ها می‌باشد. اطلاعات سنسورها و سیستم‌های اینترنت اشیا به شرکت‌ها کمک می‌کند تا دید بهتری نسبت به کارایی سیستم داشته باشند. در این پژوهش روشی مبتنی بر یادگیری عمیق و شبکه عصبی عمیق سی‌لایه‌ای برای تشخیص فعالیت‌های انسانی روی مجموعه داده تشخیص فعالیت دانشگاه فوردهام ارائه شده است. این مجموعه داده دارای بیش از یک میلیون سطر در شش کلاس برای تشخیص فعالیت در اینترنت اشیا است. بر اساس نتایج به دست آمده، مدل پیشنهادی ما در راستای تشخیص فعالیت‌های انسانی در معیارهای ارزیابی مورد نظر کارایی 90 درصد و میزان خطای 2/2 درصد را داشت. نتایج به دست آمده نشان از عملکرد خوب و مناسب یادگیری عمیق در تشخیص فعالیت است. Manuscript profile