One of the most widespread and important applications in wireless sensor networks (WSNs) is the continuous data collection, such as monitoring the variety of ambient temperature and humidity. Due to the sensor nodes with a limited energy supply, the reduction of energy consumed in the continuous observation of physical phenomenon plays a significant role in extending the lifetime of WSNs. However, the high redundancy of sensing data leads to great waste of energy as a result of over-deployed sensor nodes. In this paper, we develop a structure fidelity data collection (SFDC) framework leveraging the spatial correlations between nodes to reduce the number of the active sensor nodes while maintaining the low structural distortion of the collected data. A structural distortion based on the image quality assessment approach is used to perform the nodes work/sleep scheduling, such that the number of the working nodes is reduced while the remainder of nodes can be put into the low-power sleep mode during the sampling period. The main contribution of SFDC is to provide a unique perspective on how to maintain the data fidelity in term of structural similarity in the continuous sensing applications for WSNs. The simulation results based on synthetic and real world datasets verify the effectiveness of SFDC framework both on energy saving and data fidelity.

In a noisy manufacturing environment, range-based target localization for wireless sensor networks (WSNs) experiences various variations in range measurements, thus causing the unsatisfactory results. Starting from an empirical observation on the existing various industrial noise distribution, in this paper, a diffusion Gauss-Newton (GN) algorithm with cooperation strategy is proposed for solving target localization Non-Linear Least Squares (NLLS) problem in a WSN. The proposed algorithm has an equalization effect on the unbalance noise distribution over the network by aggregating the global estimates into local GN update via diffusion strategy. When facing a hostile industrial environment where the ambient noise is heterogeneous or has the sudden changes across partial nodes, the significant performance degradation is produced by diffusion GN. To solve the problem, we propose further an improved version of diffusion GN, which is adaptive to sudden changes on noisy range measurements. Instead of using a static combiner, the new algorithm leverages the evolutionary game theory (EGT) to assign a time-varying weight for the estimate from each neighboring node based on the individual range error. Consequently, the good estimates from the neighbors with high SNR have a larger weight in the combiner than the bad estimates caused by the low SNR or high noise. Some simulation examples show that the standard diffusion GN is effective in stationary noise and its improved version provides the adaptation to changing noisy environment.

Energy harvesting wireless sensor network (EH-WSN) is promising in applications, however the frequent occurrence of temporal death of nodes, due to the limited harvesting capability, presents a difficulty in meeting the quality-of-service requirements of the network. For a node with temporal death in an EH-WSN, this paper presents a new model, which consists of, a Markov model to trace the energy harvesting process, a queuing analytical model to model the working mechanism of the sensor node and a continuous fluid process to capture the evolution of the residual energy in the EH-WSN node. Using the Markov fluid queue (MFQ) theory, we discuss various performance aspects of the EH-WSN node with temporal death, including the temporal death occurrence probability, the probability density of the residual energy, the stationary energy consumption, the queue length distribution in the data buffer, the packet blocking probability, and so on. In order to obtain the dropping probability of a given packet, based on the structure of the MFQ, we develop an auxiliary MFQ and derive the formulations of two types of the packet dropping probabilities, i.e., the packet dropping probability due to energy depletion and that due to channel error. Numerical examples are provided to illustrate the theoretical findings, and new insights into understanding the impacts of the parameters on the performance metrics are presented.

For the perpetual wireless sensor network (WSN), energy harvesting (EH) technology is emerging as a promising solution. However, the randomness and the instability of the harvested energy may lead to the occurrence of the temporal death, which is harmful to the functions of the WSN, and has negative impact on the quality of service of the network. With temporal death being taken into account, this paper proposes a novel and overall framework, namely, a multi-layer Markov fluid queue (MLMFQ) model, for modeling and analyzing the data transmission nature of the EH devices (EHDs). We formulate the model of the whole system in terms of MLMFQ, and obtain the stead-state probabilities of the EHD. We study the issue of how to maximize the steady-state average reward rate of the reported data packets. We then propose an optimization model, in which one can maximize the overall steady-state average reward rate with relation to transmission policy under the specific constraints. On this basis, we are able to propose an algorithm for calculating the optimal transmission policy of the EHD with temporal death. We validate our results using a numerical example and report some interesting findings from our numerical studies.

Internet of Things (IoT) still faces many challenges, one of which is how to efficiently and effectively allocate the cloud resources in order to obtain the desired quality of service (QoS) for the IoT users and communication domains. To face the above challenges, this paper firstly identifies that there is an issue of cost performance trade-off, which is stemmed from various limited resources in the IoT systems and the competing services requirements. We secondly propose a nonlinear optimization model that attacks the resource allocation problem. Within the constraints of resources and service demands, this model seeks to maximize the suggested IoT cost performance ratio. We then find that the problem can be treated as a quasi-concave maximization problem. We are able to reduce the original problem to a pseudo-concave maximization problem and thereby identify a local solution, which is proved to be exactly the desired global one. We therefore propose a feasible direction method and design its corresponding algorithm to yield the desired solution. Finally, we provide a numerical example to demonstrate the theoretical findings of resource allocation and the proposed algorithm for the cloud computing based on IoT systems. The mathematical results and method proposed in this paper can act as designing guidelines for resource allocation and managements, computing scheduling and networking protocol designing in the cloud infrastructure of IoT.

The current Internet is dramatically suffering the Distributed Denial of Service (DDoS) attacks. In this paper, we investigate an Internet transmission control protocol/active queue management (TCP/AQM) router subject to DDoS attacks. We utilize the time delay control theory to analyze the dynamics of the congestion control windows and the queues at the router. We derive some explicit conditions under which the TCP/AQM system under DDoS attacks is asymptotically stable. We discuss the convergence of the queue lengths in the router. Our results suggest that, if the network parameters in the TCP window updating and control parameters in the AQM algorithm satisfy certain conditions, the TCP/AQM system is stable and its queue lengths can converge to any given target. This result is important and promising in terms of applications in that, when the DDoS attacked traffic is differentiated from the legitimate traffic, one is able to choke the DDoS attacks by limiting their rates and then to improve the bandwidth usage of the normal flows. We illustrate the theoretical results using the network simulation platform ns2 and demonstrate that the controlled network can achieve good performance, enhancing the Internet robustness and performance against DDoS attacks.