Data collection is the basic functions of the Internet of Things (IoT), in which the sensed data are concentrations from sensor nodes to the sink, with a timely style, so the smart response can be done for emergency. The goal of multi-modal sensor data fusion is to obtain simple and accurate data to enhance system reliability and fault tolerance. Energy efficiency and small delay are the most important indicators which govern the performance of IoT. Convergecast is a low-latency data collection strategy based on effective time division multiple access (TDMA), in which each sensor node generates a packet, and m packets can aggregate to a packet. However, in most practical networks, sensor nodes do not necessarily generate packets during each data collection cycle, but instead generate packets from time to time. In the previous convergecast strategy, each node was fixedly allocated a slot, which increased the delay and wasted energy. A delay and energy-efficient data collection (DEEDC) scheme-based matrix filling theory is proposed to collect data in a randomly generated WSNs with minimum delay and energy consumption. The DEEDC scheme uses a clustering approach. For each cluster, the number of slots required for transmission is calculated by matrix filling theory, not the number of nodes that actually generate data. This ensures that data can be collected in a network with randomly generated data (number of slots ≤ number of nodes), thereby avoiding the allocation of slots for each node and the acquisition of redundant data to lead to the wastage of time and energy. Based on the above, a mixed slot scheduling strategy is proposed to construct energy and delay-efficient, collision-free schedule scheme. After extensive theoretical analysis, by using the DEEDC scheme, the delay is reduced by about 50~80%, and the energy consumed is reduced by about 40~57%.

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Wireless sensor networks are the foundation of cyber physical systems. In loss and delay sensitive wireless sensor networks, there is much room for improving the performance of opportunistic routing scheme. In this article, an adaptive duty cycle control-based opportunistic routing scheme was put forward to improve the performance of the opportunistic routing scheme. Our main work is as follows: (a) the proposed duty cycle control-based opportunistic routing scheme implements the opportunistic routing in the duty cycle-based wireless sensor networks. Compared with the opportunistic routing scheme in the non-duty cycle mode, the duty cycle control-based opportunistic routing scheme can effectively reduce the energy consumption by 55.64%, although the performance in the delay is degraded. (b) Next, adaptive duty cycle control-based opportunistic routing scheme is proposed to reduce the delay of nodes in duty cycle control-based opportunistic routing. In adaptive duty cycle control-based opportunistic routing scheme, the node's duty cycle is increased in the area where the energy has remaining, so that the network delay can be reduced without reducing the network lifetime. Compared with duty cycle control-based opportunistic routing scheme, the maximum end-toend delay of the node in the network can be reduced by 69.83%.

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