The oceans cover more than 71% of the Earth’s sur-face and have a surging amount of data. It is of great significance to seek energy-effective and ultra-reliable communication and transmis-sion mechanism for effectively gathering the abundant maritime data. In this paper, we propose an Autonomous-underwater-vehicle (AUV) assisted data gathering scheme based on Clustering and Matrix Com-pletion (ACMC) to improve the data gathering efficiency in underwa-ter wireless sensor network (UWSN). Specifically, we first improve the K-means algorithm by adopting the Elbow method to determine the optimal K and setting a distance threshold to select the separate initial cluster centers. Then we introduce a two-phase AUV trajectory optimization mechanism to effectively reduce the trajectory length of the AUV. In the first phase, the optimized trajectory of the AUV is planned by adopting the greedy algorithm. In the second phase, the ordinary noodes close to the AUV trajectory are selected as secondary cluster heads to share the workload of cluster heads. Finally, we pre-sent an in-cluster data collection mechanism based on matrix comple-tion. Extensive experiment validates the effectiveness of our proposed scheme in terms of energy and data collection delay.

In this paper, a Probe Route based Defense Sinkhole Attack (PRDSA) scheme is proposed to resist Sinkhole attack and guarantee security for IoT, which is the first work that can detect, bypass and locate the sinkhole at the same time. The PRDSA scheme proposes a routing mechanism combining the far-sink reverse routing, equal-hop routing, and minimum hop routing, which can effectively circumvent the sinkhole attacks and find a safe route to the real sink, so that the scheme can achieve better sinkhole detection. More importantly, the PRDSA scheme overcomes the limitation of previous schemes that they cannot locate the sinkhole. During the detection of the sinkhole attack, the PRDSA scheme requires the nodes and the sink node to return the signature of the information (e.g., IDs, etc.), so that the location of the sinkhole can be determined. Furthermore, the PRDAS scheme mainly utilizes the characteristics of network energy consumption. The probe route of sinkhole attack mainly occurs in the region where the remaining energy exists. Thus, the PRDSA scheme has little impact on the network lifetime. Theory and experiments show that this scheme can achieve better performance than existing schemes in term of network security and lifetime.

Industrial Communication Systems (ICSs) have become an important part of many automated applications. Smart sensors for industrial communication systems have attracted much attention. Using smart sensors for industrial communication systems can allow the surrounding world to connect with the production equipment to reduce human resource requirements. However, due to their operating nature, smart sensors are often unattended and prone to different kinds of attacks. An on-off attack is one behavior that can cause serious damage to wireless sensor networks (WSNs) in industrial communication systems. To ensure network security, a trust joint light probe-based defense (TLPD) mechanism is proposed in which each smart sensor node selects a smart sensor node with high trust as the next hop to improve network security. The TLPD scheme is constituted by three major components, including Light probe routing, Trust Estimation and Trust-aware routing. Light probe routing is adopted for increasing the operation times of malicious nodes, trust evaluation scheme evaluates nodes trust according to operation behavior of nodes. Thus, the discrimination degree of node trust between temporary errors and disguised malicious behaviors can be increased compared to existing trust evaluation schemes. Thus, a malicious smart sensor node can be quickly and easily identified. The analytical results of the TLPD mechanism assessed is with a simulation experiment.

Millions of sensors are deployed to monitor the smart grid. They consume huge amounts of energy in the communication infrastructure. Therefore, the establishment of an energy-efficient medium access control (MAC) protocol for sensor nodes is challenging and urgently needed. The Quorum-based MAC protocol independently and adaptively schedules nodes' wake-up times and decreases idle listening and collisions, thereby increasing the network throughput and extending the network lifetime. A novel Quorum time slot adaptive condensing (QTSAC)-based MAC protocol is proposed for achieving delay minimization and energy efficiency for the wireless sensor networks (WSNs). Compared to previous protocols, the QTSAC-based MAC protocol has two main novelties: 1) It selects more Quorum time slots (QTSs) than previous protocols in the area that is far from the sink according to the energy consumption in WSNs to decrease the network latency and 2) It allocates QTSs only when data are transmitted to further decrease the network latency. Theoretical analyses and experimental results indicate that the QTSAS protocol can greatly improve network performance compared with existing Quorum-based MAC protocols. For intermediate-scale wireless sensor networks, the method that is proposed in this paper can enhance the energy efficiency by 24.64%-82.75%, prolong the network lifetime by 58%-27.31%, and lower the network latency by 3.59%-29.23%.

Real-time surveillance systems with green wireless sensor networks (WSNs) are vital for maintaining high energy efficiency in many situations. This paper considers a scenario utilizing green WSNs to monitor the situation of Internet of Things (IoT), which constitute one of the most crucial sources of electricity consumption in information and communications technologies (ICT). More specifically, we focus on optimizing the cluster structure to minimize the delay and energy consumption for aggregation convergecast in green WSNs. We first find the optimal value of the network cluster radius for minimizing the delay through theoretical analysis. We then propose a novel cluster network architecture in which clusters that are far from the sink are small, allowing inter-cluster data aggregation to be processed earlier, and clusters that are near the sink are relatively large to allow more time for intra-cluster data aggregation. Hence, the sensor nodes can be scheduled in consecutive time slots to reduce the number of state transitions, consequently achieving the goal of minimizing both delay and energy consumption. Simulation results indicate that the proposed Algorithm outperforms previously reported solutions in terms of both schedule length and lifetime, thereby demonstrating its effectiveness.

For the cloud computing based on Software Defined Networks (SDN), a larger amount of data is collected to cloud for analysis, which will cause the larger amount of redundancy data and longer services response time due to the capacity-limited internet. To solve this problem, a novel services orchestration and data aggregation framework (SODA) is proposed, which can orchestrate data as services and aggregate data packets to reduce data redundancy and services response delay. In SODA, the network is divided into three layers. (1) Data centers layer (DCL). Data centers release software with specific function to all devices in the network, devices orchestrate data as services and aggregate data packets using software to reduce services response delay. (2) Middle routing layer (MRL). The routing path of data packets in this layer is adjusted according to the correlation of data packets and routing distance. The correlation of data packets is higher and routing distance is short, the probability that data packets are transmitted along same routing path is higher to reduce redundancy data. (3) Vehicle network layer (VNL), mobile vehicles are used to transmit data packets and services among devices. A series of experiments and simulation are conducted. The results illustrate that the proposed scheme has better performance compared with traditional scheme.