At the dawn of big data and 5G networks, end-to-end communication with large amounts of data between mobile devices is difficult to be implemented through the traditional face-to-face transmission mechanism in social networks. Consequently, opportunistic social networks proposed that message applications should choose proper relay nodes to perform effective data transmission processes. At present, several routing algorithms, based on node similarity, attempt to use the contextual information related to nodes and the special relationships between them to select a suitable relay node among neighbors. However, when evaluating the similarity degree between a pair of nodes, most existing algorithms in opportunistic social networks pay attention to only a few similar factors, and even ignore the importance of mobile similarity in the data transmission process. To improve the transmission environment, this study establishes a fuzzy routing-forwarding algorithm (FCNS) exploiting comprehensive node similarity (the mobile and social similarities) in opportunistic social networks. In our proposed scheme, the transmission preference of the node is determined through the fuzzy evaluation of mobile and social similarities. The suitable message delivery decision is made by collecting and comparing the transmission preference of nodes, and the sustainable and stable data transmission process is performed through the feedback mechanism. Through simulations and the comparison of social network algorithms, the delivery ratio in the proposed algorithm is 0.85 on average, and the routing delay and network overhead of this algorithm are always the lowest.

In most developing countries, it has become a severe challenge for the limited medical resources and outdated healthcare technology to meet the high demand of large population. From the perspective of social development, this unbalanced healthcare system in developing counties has also exacerbated the contradiction between physicians and patients, particularly those suffering from malignant diseases (such as prostate cancer). Rapid improvements in artificial intelligence, computing power, parallel operation, and data storage management have contributed significantly to a credible medical data decision-making on the detection, diagnosis, treatment, and prognosis of malignant diseases. Consequently, to address these existing problems in the current healthcare field of developing countries, this paper proposes a novel big medical data decision-making model exploiting fuzzy inference logic for prostate cancer in developing countries, constructing an intelligent medical system for disease detection, medical data analysis and fusion, treatment recommendations, and risk management. Based on 1 933 535 items of hospitalization information from over 8000 prostate cancer cases in China, the experimental results demonstrate that the intelligent medical system could be adopted to assist physicians and medical specialists in coming up with a more dependable diagnosis scheme.

With the development of big data and high-speed communication networks, traditional end-to-end transmission mechanisms in social networks are difficult to achieve large amounts of data communication between mobile devices. Therefore, the implementation of effective data transmission in social networks requires "opportunity". Opportunistic social networks suggest choosing the most appropriate next hop nodes for effective data transmission. Most existing routing algorithms attempt to use the interest points of nodes and the social relationships between them to choose optimal relay nodes among neighbors. However, most community-based algorithms take node attributes and social relations into account but fail to consider the energy consumption of inefficient nodes which accounts for a large part of routing cost. To improve the transmission strategy, this work proposes an effective transmission strategy based on node socialization (ETNS), which divides nodes in the network into several different communities. The proposed scheme also involves a community reduction method that removes some inefficient nodes according to the attributes of optimal relay nodes. Simulation results show that the packet delivery ratio of ETNS is 13% higher than epidemic algorithm, and ETNS also has lower transmission delay and routing overhead.

The arrival of 5G is accompanied by massive data transmission between mobile devices (MDs) and huge transmission energy consumption in the wireless network. Therefore, how message applications select the appropriate relay MDs to complete the efficient data transmission process in opportunistic mobile social networks (OMSNs)? At present, designing an efficient routing-forwarding algorithm is extremely challenging. Some routing-forwarding algorithms choose appropriate nodes as relay nodes based on the similarity between nodes, but most existing routing-forwarding algorithms only consider a few similar factors and even completely ignore the importance of movement similarity in data transmission of node. In particular, existing routing-forwarding algorithms will bring extra energy consumption to the nodes in the wireless network, and excessive energy consumption will further affect delay and data transmission efficiency. In order to solve the problems in the existing strategies, we apply mobile edge computing (MEC) to OMSNs, and we propose the fuzzy reasoning routing-forwarding (FRRF) algorithm that integrates movement and social similarity in MEC-based OMSNs. In detail, the fuzzy evaluation of movement and social similarity is integrated to determine the transmission priority value between MDs, and finally the transmission priority between MDs is compared to make transmission decision. Through simulation experiment and comparison with other algorithms, the correctness of the theoretical analysis and the efficiency of the FRRF algorithm in energy consumption, delay and transmission efficiency are verified.

In the social scene of opportunistic networks, message applications find suitable relay nodes or certain transmission destinations from the surrounding neighbors through specific network addresses of users. However, at the dawn of big data and 5G networks, the variational location information of nodes is difficult to be available to mobile devices all the time, and a long wait for the destination may cause severe end-to-end delay. To improve the transmission environment, this study constructs an efficient routing-delivery scheme (Predict and Forward) based on node profile for the opportunistic networks. The node profile effectively characterizes nodes by analyzing and comparing their attributes instead of network addresses, such as physical characteristics, places of residence, workplaces, occupations or hobbies. According to the optimal stopping theory, this algorithm implements the optimal transmission for Prelearn messages by dividing the complex data transmission process into two different phases (Predict and Forward). Through simulations and the comparison of routing algorithms in opportunistic networks, the proposed strategy increases the delivery ratio by 80% with the traditional methods on average, and the average end-to-end delay in this algorithm is the lowest.

Mobile Edge Computing (MEC) has evolved into a promising technology that can relieve computing pressure on wireless devices (WDs) in the Internet of Things (IoT) by offloading computation tasks to the MEC server. Resource management and allocation are challenging because of the unpredictability of task arrival, wireless channel status and energy consumption. To address such a challenge, in this paper, we provide an energy-efficient joint resource management and allocation (ECM-RMA) policy to reduce time-averaged energy consumption in a multi-user multi-task MEC system with hybrid energy harvested WDs. We first formulate the time-averaged energy consumption minimization problem while the MEC system satisfied both the data queue stability constraint and energy queue stability constraint. To solve the stochastic optimization problem, we turn the problem into two deterministic sub-problems, which can be easily solved by convex optimization technique and linear programming technique. Correspondingly, we propose the ECM-RMA algorithm that does not require priori knowledge of stochastic processes such as channel states, data arrivals and green energy harvesting. Most importantly, the proposed algorithm achieves the energy consumption-delay trade-off as $\left[\mathcal{O}\right(1/V),\mathcal{O}(V\left)\right]$ . V, as a non-negative weight, which can effectively control the energy consumption-delay performance. Finally, simulation results verify the correctness of the theoretical analysis and the effectiveness of the proposed algorithm.