In this paper, a three-node wireless powered communication system is studied, where a power receiver harvests energy from a wireless power transmitter via wireless power transfer in the downlink and then executes information transfer in the uplink. According to this simple harvest-then-transmit protocol, the power receiver cannot harvest energy and send information simultaneously. Thus, it is necessary to investigate the tradeoff between wireless power transfer and information transfer to obtain good performance. The goal of this paper is to achieve the maximum throughput by balancing the time duration between the wireless power transfer phase and the information transfer phase while satisfying the energy causality constraint, the time duration constraint, and the quality-of-service constraint (i.e., the symbol error rate is lower than a target value). By solving the optimization problem, an optimal time allocation can be obtained. Simulation results demonstrate the effectiveness of the solution.

With the repaid development of Internet of Vehicles (IoV), more available resources and energy-efficient optimizations in resources scheduling are exactly required for large-scale network implementation for sustainable development. We observe that parked vehicles (PVs) have rich and underutilized resources for task execution. By scheduling them as general computing nodes to undertake computation tasks, we introduce a new computing paradigm, named by Parked Vehicular Computing (PVC). There exists some challenging issues to be addressed for the facilitation of PVC. In particular, an incentive mechanism is needed to offer optimized reward for PVs with the consideration of their parking time and energy consumption. In this paper, we investigate an energy-efficient PVC paradigm, and we design a contract-based incentive mechanism to motivate PVs to contribute their idle on-board resources. The PVs are classified into different types according to their parking time. Then, the designed contracts are assigned to different types of PVs. To realize the incentive mechanism, the optimization problem with the contract design is formulated to maximize the utility of the service provider. For optimal contract design, we solve the simplified problem by using Lagrangian multiplier method. Numerical results indicate that the proposed PVC with optimal contract design outperforms existing work in improving social welfare of resource scheduling, which takes quality-of-service and overall energy consumption into consideration. We also demonstrate that the contract-based incentive mechanism is energy-efficient and effective.

In this paper, we present interference models for cognitive radio (CR) networks employing various interference management mechanisms including power control, contention control or hybrid power/contention control schemes. For the first case, a power control scheme is proposed to govern the transmission power of a CR node. For the second one, a contention control scheme at the media access control (MAC) layer, based on carrier sense multiple access with collision avoidance (CSMA/CA), is proposed to coordinate the operation of CR nodes with transmission requests. The probability density functions (PDFs) of the interference received at a primary receiver from a CR network are first derived numerically for these two cases. For the hybrid case, where power and contention controls are jointly adopted by a CR node to govern its transmission, the interference is analyzed and compared with that of the first two schemes by simulations. Then, the interference PDFs under the first two control schemes are fitted by log-normal PDFs to reduce computation complexity. Moreover, the effect of a hidden primary receiver on the interference experienced at the receiver is investigated. It is demonstrated that both power and contention controls are effective approaches to alleviate the interference caused by CR networks. Some in-depth analysis of the impact of key parameters on the interference of CR networks is given as well.

Potable water distribution networks are important infrastructures of modern cities. A good design of the network can not only reduce the construction expenditure but also provide reliable service. Nowadays, the scale of the water distribution network of a city grows dramatically along with the city expansion, which brings heavy pressure to its optimal design. In order to solve the large-scale water distribution network optimization problem, a cooperative co-evolutionary algorithm is proposed in this paper. First, an iterative trace-based decomposition method is specially designed by utilizing the information of water tracing to divide a large-scale network into small sub-networks. Since little domain knowledge is required, the decomposition method has great adaptability to multiform networks. Meanwhile, during optimization, the proposed algorithm can gradually refine the decomposition to make it more accurate. Second, a new fitness function is devised to handle the pressure constraint of the problem. The function transforms the constraint into a part of the objective to punish the infeasible solutions. Finally, a new suite of benchmark networks are created with both balanced and imbalanced cases. Experimental results on a widely-used real network and the benchmark networks show that the proposed algorithm is promising.

This paper presents $\mathit {wChain}$ , a blockchain protocol specifically designed for multihop wireless networks that deeply integrates wireless communication properties and blockchain technologies under the realistic SINR model. We adopt a hierarchical spanner as the communication backbone to address medium contention and achieve fast data aggregation within $O(\log N\log \Gamma)$ slots where $N$ is the network size and $\Gamma $ refers to the ratio of the maximum distance to the minimum distance between any two nodes. Besides, $\mathit {wChain}$ employs data aggregation and reaggregation as well as node recovery mechanisms to ensure efficiency, fault tolerance, persistence, and liveness. The worst-case runtime of $\mathit {wChain}$ is upper bounded by $O(f\log N\log \Gamma)$ , where $f=\lfloor \frac {N}{2} \rfloor $ is the upper bound of the number of faulty nodes. To validate our design, we conduct both theoretical analysis and simulation studies. The results not only demonstrate the nice properties of $\mathit {wChain}$ , but also point to a large new space for the exploration of blockchain protocols in wireless networks.

With the rapid development of large-scale complex networks and proliferation of various social network applications, the amount of network traffic data generated is increasing tremendously, and efficient anomaly detection on those massive network traffic data is crucial to many network applications, such as malware detection, load balancing, network intrusion detection. Although there are many methods around for network traffic anomaly detection, they are all designed for single machine, failing to deal with the case that the network traffic data are so large that it is prohibitive for a single computer to store and process the data. To solve these problems, we propose a parallel algorithm based on Isolation Forest and Spark for network traffic anomaly detection. We combine the advantages of Isolation Forest algorithm in network traffic anomaly detection and big data processing capability of Spark technology. Meanwhile, we apply the idea of parallelization to the process of modeling and evaluation. In the calculation process, by assigning tasks to multiple compute nodes, Isolation Forest and Spark can efficiently perform anomaly detection and evaluation process. By this way, we can also solve the problem of computation bottleneck on single machine. Extensive experiments on real world datasets show that our Isolation Forest and Spark is efficient and scales well for anomaly detection on large network traffic data.