Low power wide area (LPWA) technologies are strongly recommended as the underlying networks for Internet of things (IoT) applications. They offer attractive features, including wide-range coverage, long battery life and low data rates. This paper reviews the current trends in this technology, with an emphasis on the services it provides and the challenges it faces. The industrial paradigms for LPWA implementation are presented. Compared with other work in the field, this survey focuses on the need for integration among different LPWA technologies and recommends the appropriate LPWA solutions for a wide range of IoT application and service use-cases. Opportunities created by these technologies in the market are also analyzed. The latest research efforts to investigate and improve the operation of LPWA networks are also compared and classified to enable researchers to quickly get up to speed on the current status of this technology. Finally, challenges facing LPWA are identified and directions for future research are recommended.

Swarm Intelligence is a metaheuristic optimization approach that has become very predominant over the last few decades. These algorithms are inspired by animals' physical behaviors and their evolutionary perceptions. The simplicity of these algorithms allows researchers to simulate different natural phenomena to solve various real-world problems. This paper suggests a novel algorithm called Donkey and Smuggler Optimization Algorithm (DSO). The DSO is inspired by the searching behavior of donkeys. The algorithm imitates transportation behavior such as searching and selecting routes for movement by donkeys in the actual world. Two modes are established for implementing the search behavior and route-selection in this algorithm. These are the Smuggler and Donkeys. In the Smuggler mode, all the possible paths are discovered and the shortest path is then found. In the Donkeys mode, several donkey behaviors are utilized such as Run, Face & Suicide, and Face & Support. Real world data and applications are used to test the algorithm. The experimental results consisted of two parts, firstly, we used the standard benchmark test functions to evaluate the performance of the algorithm in respect to the most popular and the state of the art algorithms. Secondly, the DSO is adapted and implemented on three real-world applications namely; traveling salesman problem, packet routing, and ambulance routing. The experimental results of DSO on these real-world problems are very promising. The results exhibit that the suggested DSO is appropriate to tackle other unfamiliar search spaces and complex problems.

<div>Low-power wide area (LPWA) technologies are strongly recommended as the underlying</div><div>networks for Internet of Things (IoT) applications. They offer attractive features, including wide-range</div><div>coverage, long battery life, and low data rates. This paper reviews the current trends in this technology,</div><div>with an emphasis on the services it provides and the challenges it faces. The industrial paradigms for LPWA</div><div>implementation are presented. Compared with other work in the field, this paper focuses on the need for</div><div>integration among different LPWA technologies and recommends the appropriate LPWA solutions for a</div><div>wide range of IoT application and service use cases. Opportunities created by these technologies in the</div><div>market are also analyzed. The latest research efforts to investigate and improve the operation of LPWA</div><div>networks are also compared and classified to enable researchers to quickly get up to speed on the current</div><div>status of this technology. Finally, challenges facing LPWA are identified and directions for future research</div><div>are recommended.</div>

At present, deep learning is widely used in a broad range of arenas. A convolutional neural networks (CNN) is becoming the star of deep learning as it gives the best and most precise results when cracking real-world problems. In this work, a brief description of the applications of CNNs in two areas will be presented: First, in computer vision, generally, that is, scene labeling, face recognition, action recognition, and image classification; Second, in natural language processing, that is, the fields of speech recognition and text classification.

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