Internet of Things (IoT) is considered as a key enabler of health informatics. IoT-enabled devices are used for in-hospital and in-home patient monitoring to collect and transfer biomedical data pertaining to blood pressure, electrocardiography (ECG), blood sugar levels, body temperature, etc. Among these devices, wearables have found their presence in a wide range of healthcare applications. These devices generate data in real-time and transmit them to nearby gateways and remote servers for processing and visualization. The data transmitted by these devices are vulnerable to a range of adversarial threats, and as such, privacy and integrity need to be preserved. In this paper, we present LightIoT, a lightweight and secure communication approach for data exchanged among the devices of a healthcare infrastructure. LightIoT operates in three phases: initialization, pairing, and authentication. These phases ensure the reliable transmission of data by establishing secure sessions among the communicating entities (wearables, gateways and a remote server). Statistical results exhibit that our scheme is lightweight, robust, and resilient against a wide range of adversarial attacks and incurs much lower computational and communication overhead for the transmitted data in the presence of existing approaches.

Heterogeneous ultra-dense networking (HUDN) with energy harvesting technology is a promising approach to deal with the ever-growing traffic that can severely impact the power consumption of small-cell networks. Unfortunately, the amount of harvested energy, which depends on the transmission environment, is highly random and difficult to predict. Since there may be multiple sources of energy in the HUDN, e.g., macro base stations or TV towers, the challenging issue is when and where to harvest energy. Optimally controlling the HUDN can profoundly influence the performance of both data transmission and energy harvesting. However, the working pattern of individual small cell base stations needs to be determined in every time slot. To find an optimal solution in a highly random environment we propose reinforcement learning methods, such as deep deterministic policy gradient (DDPG) and wolpertinger DDPG (W-DDPG). Since the action space is large and discrete for the controlling tasks, a W-DDPG algorithm has been found to be the best approach. The simulation results verify that, compared with the original DDPG algorithm and deep Q-learning, the proposed W-DDPG method can achieve a superior performance in terms of both energy efficiency and throughput.

In recent years, edge computing has emerged as an effective solution to extend cloud computing and satisfy the demand of applications for low latency. However, with today's explosion of innovative applications ( augmented reality, natural language processing, virtual reality), processing services for mobile and smart devices have become computation-intensive, consisting of multiple interconnected computations. This coupled with the need for delay-sensitivity and high quality of service put massive pressure on edge servers. Meanwhile, tasks invoking these services may involve similar inputs that could lead to the same output. In this paper, we present , an efficient computation reuse architecture for edge computing. enables edge servers to reuse previous computations while scheduling dependent incoming computations. We provide an analytical model for computation reuse joined with dependent task offloading and design a novel computing offloading scheduling scheme. We also evaluate the efficiency and effectiveness of via synthetic and real-world datasets. Our results show that is able to reduce the task execution time up to 66% based on a synthetic dataset and up to 50% based on a real-world dataset.