This paper investigates the issues of packet alignment and consistency among PLC devices based on industrial network environments, aiming to ensure the integrity and accuracy of packets from sender to receiver. To achieve this goal, we propose an anomaly detection method that combines the DBSCAN clustering algorithm with the 3-sigma principle to identify and handle abnormal packets that may occur during transmission. By comparing the data between the sending and receiving ends, and analyzing based on timestamps and data content, we validate the alignment of packets in the network environment. Experimental results demonstrate that the proposed method effectively detects and corrects packet loss or delay jitter, thereby enhancing the reliability of communication between PLC devices and the consistency of data transmission. The scheme presented in this paper enables quicker and more precise identification of packet loss and delays, adapting well to various network load conditions. Further experimental analysis indicates that this method excels in reducing both false positive and false negative rates, and it exhibits good scalability, making it applicable to data alignment and consistency verification in other industrial automation scenarios. Ultimately, this novel solution provides stability and accuracy for data transmission among devices in a network environment.

The assisted download is an effective method solving the problem that the coverage range is insufficient when Wi-Fi access is used in VANET. For the low utilization of time-space resource within blind area and unbalanced download services in VANET, this paper proposes an approximate global optimum scheme to select vehicle based on WebGIS for assistance download. For WebGIS, this scheme uses a two-dimensional matrix to respectively define the time-space resource and the vehicle selecting behavior, and uses Markov Decision Process to solve the problem of time-space resource allocation within blind area, and utilizes the communication features of VANET to simplify the behavior space of vehicle selection so as to reduce the computing complexity. At the same time, Euclidean Distance(Metric) and Manhattan Distance are used as the basis of vehicle selection by the proposed scheme so that, in the case of possessing the balanced assisted download services, the target vehicles can increase effectively the total amount of user downloads. Experimental results show that because of the wider access range and platform independence of WebGIS, when user is in the case of relatively balanced download services, the total amount of downloads is increased by more than 20%. Moreover, WebGIS usually only needs to use Web browser (sometimes add some plug-ins) on the client side, so the system cost is greatly reduced.

Real-time traffic estimation focuses on predicting the travel time of one travel path, which is capable of helping drivers selecting an appropriate or favor path. Statistical analysis or neural network approaches have been explored to predict the travel time on a massive volume of traffic data. These methods need to be updated when the traffic varies frequently, which incurs tremendous overhead. We build a system RealTER⁢e⁢a⁢l⁢T⁢E, implemented on a popular and open source streaming system StormS⁢t⁢o⁢r⁢m to quickly deal with high speed trajectory data. In RealTER⁢e⁢a⁢l⁢T⁢E, we propose a locality-sensitive partition and deployment algorithm for a large road network. A histogram estimation approach is adopted to predict the traffic. This approach is general and able to be incremental updated in parallel. Extensive experiments are conducted on six real road networks and the results illustrate RealTE achieves higher throughput and lower prediction error than existing methods. The runtime of a traffic estimation is less than 11 seconds over a large road network and it takes only 619619 microseconds for model updates.

Due to the resource constraint, in wireless sensor network, the node processing ability, wireless bandwidth and battery capacity and other resources are scarcer. For improving the energy efficient and extend the lifetime of the network, this paper proposes a novel algorithm with the distributed and energy-efficient for collecting and aggregating data of wireless sensor network. In the proposed protocol, nodes can autonomously compete for the cluster head based on its own residual energy and the signal strength of its neighbouring nodes. To reduce the energy overhead of cluster head nodes, with a multi-hop way among cluster heads, the collected data from cluster heads is sent to a designated cluster head so as to further send these data to a base station. For improving the performance of the proposed protocol, a new cluster coverage method is proposed to fit the proposed protocol so that when the node density increases, network lifetime can be increased linearly as the number of nodes is increased. Simulations experiments show that network lifetime adopting the proposed protocol is sharply increased. And, the proposed protocol makes all the nodes die (network lifetime is defined as the death of last one node) in the last 40 rounds so that networks adopting the proposed protocol have higher reliability than networks adopting compared protocols.

Key management is the basis of the security mechanism for wireless sensor networks and services, and random key pre-distribution is the most effective key management mechanism at present. However, there is a potential challenge to most current random key pre-distribution strategies: it is difficult to achieve both ideal network security connectivity and network survivability. In this paper, we present a novel random key pre-distribution scheme based on the hash chain. By adjusting certain system parameters, such as the hash chain length, the number of common auxiliary nodes and the number of hash chains, a sensor node only need to preload a few of keys, making it possible to establish the pairwise key with high probability among its neighboring nodes. The proposed scheme can still maintain strong network survivability even if there are many compromised nodes. The theoretical analysis and simulation experiments show that the proposed scheme is not only effective and secure, but also scalable.