An Energy-Efficient Scheme for IoT Networks

With the advent of the Internet of Things era, "things-things interconnection" has become a new concept, that is, through the informatization and networking of the physical world, the traditionally separated physical world and the information world are interconnected and integrated. Different from the concept of connecting people in the information world in the Internet, the Internet of Things extends its tentacles to all aspects of the physical world. The proposed algorithm considers the periodical uplink data transmission in IEEE 802.11ah LWPAN and a real-time raw settings method is used. The uplink channel resources were divided into Beacon periods after the multiple nodes send data to the access point. First, the access point predicted the next data uploading time during the Beacon period. In the next Beacon period, the total number of devices that will upload data is predicted. Then, the optimal read-and-write parameters were calculated for minimum energy cost and broadcasted such information to all nodes. After this, the data is uploaded according the read-and-write scheduling by all the devices. Simulation results show that the proposed algorithm effectively improved the network state prediction accuracy and dynamically adjusted the configuration parameters which results in improved network energy efficiency in the IoT environment

Intelligent and secure fog-aided internet of drones

Internet of drones (IoD), which utilize drones as Internet of Things (IoT) devices, deploys several drones in the air to collect ground information and send them to the IoD gateway for further processing. Computing tasks are usually offloaded to the cloud data center for intensive processing. However, many IoD applications require real-time processing and event response (e.g., disaster response and virtual reality applications). Hence, data processing by the remote cloud may not satisfy the strict latency requirement. Fog computing attaches fog nodes, which are equipped with computing, storage and networking resources, to IoD gateways to assume a substantial amount of computing tasks instead of performing all tasks in the remote cloud, thus enabling immediate service response. Fog-aided IoD provisions future events prediction and image classification by machine learning technologies, where massive training data are collected by drones and analyzed in the fog node. However, the performance of IoD is greatly affected by drones\u27 battery capacities. Also, aggregating all data in the fog node may incur huge network traffic and drone data privacy leakage. To address the challenge of limited drone battery, the power control problem is first investigated in IoD for the data collection service to minimize the energy consumption of a drone while meeting the quality of service (QoS) requirements. A PowEr conTROL (PETROL) algorithm is then proposed to solve this problem and its convergence rate is derived. The task allocation (which distributes tasks to different fog nodes) and the flying control (which adjusts the drone\u27s flying speed) are then jointly optimized to minimize the drone\u27s journey completion time constrained by the drone\u27s battery capacity and task completion deadlines. In consideration of the practical scenario that the future task information is difficult to obtain, an online algorithm is designed to provide strategies for task allocation and flying control when the drone visits each location without knowing the future. The joint optimization of power control and energy harvesting control is also studied to determine each drone\u27s transmission power and the transmitted energy from the charging station in the time-varying IoD network. The objective is to minimize the long-term average system energy cost constrained by the drones\u27 battery capacities and QoS requirements. A Markov Decision Process (MDP) is formulated to characterize the power and energy harvesting control process in time-varying IoD networks. A modified actor-critic reinforcement learning algorithm is then proposed to tackle the problem. To address the challenge of drone data privacy leakage, federated learning (FL) is proposed to preserve drone data privacy by performing local training in drones and sharing training model parameters with a fog node without uploading drone raw data. However, drone privacy can still be divulged to ground eavesdroppers by wiretapping and analyzing uploaded parameters during the FL training process. The power control problem of all drones is hence investigated to maximize the FL system security rate constrained by drone battery capacities and the QoS requirements (e.g., FL training time). This problem is formulated as a non-linear programming problem and an algorithm is designed to obtain the optimum solutions with low computational complexity. All proposed algorithms are demonstrated to perform better than existing algorithms by extensive simulations and can be implemented in the intelligent and secure fog-aided IoD network to improve system performances on energy efficiency, QoS, and security

Deployment of Ahmadu Bello University Zaria, Nigeria Institutional Digital Repository

The paper discussed the concept of open access initiative and its relevance to the development of digital repositories. It primarily focused on the  institutional digital repository project of the Ahmadu Bello University, Zaria, Nigeria. The University library administration setup, policy, equipment and facilities including software for the project were highlighted. The digitisation process, test running, training, system installation and the workflow developed by the institution were discussed. The successes recorded and challenges faced by the project were equally presented. The paper concluded that repositories are very important to universities in helping them showcase, manage and capture their intellectual assets as a part of their information service strategy and contribution to universal access to knowledge and information

A Pilot Program in Internet-of-things with University and Industry Collaboration: Introduction and Lessons Learned

Internet-of-Things (IoT) is one of the most prominent technological eco-systems and an engine of growth with an estimated market size of $14 Trillion to$33 Trillion by 2025 (McKinsey Global Institute). The IoT eco-system uses well-established technologies in many fields; and it adds new and often challenging requirements on extant techniques. For example, many wireless schemes are or being redesigned to address battery life and cost of solution issues. At the same time, the industry needs to hire and retrain many technical personnel to address these issues and support this newly evolving eco-system in many different markets. These facts culminate in the need for engineering students to be skilled to handle the new challenges and match the hiring market needs. As importantly, the more experienced technical personnel need to be retrained to understand this evolving eco-system. In this light, we have taken parallel symbiotic steps to address these challenges. We have piloted a course in IoT covering the most critical technologies in a typical end-to-end IoT system, including various access technologies and higher layer protocols and standards as well as prominent cloud services. Our industry partner has developed new measurement equipment to address more accurate and sensitive current draw of circuits to assist with power-frugal designs for long battery life. They have also developed a programmable board along with several experiments geared towards IoT applications. Last summer a small group of graduate students, with the guidance of a senior faculty member, used the IoT board to assess its efficacy for less experienced engineering students. The board and the associated experiments were found to be very useful and a good addition to the program. The experiments are also valuable for continuing education purposes for developing specific skills in the development of IoT systems. The team created an updated and tailored user’s manual to better serve the needs of less experienced engineering students and to alleviate the initial frustration associated with setting up the system. In this paper, we will present the experiences of the pilot program and the key points that present the enhancements of technical manual for a teaching environment. We will present the value that the IoT board and its experiments bring to the students in order to enhance their experience when learning about the IoT eco-system