For distributed estimation arising in the nonlinear least squares (NLLSs) problems over adaptive networks, where every node has the abilities of data processing and learning, only the incomplete local data are exploited by the traditional noncooperative method, thereby resulting in the degradation on estimation performance. In this article, a cooperative diffusion strategy is proposed by using a Gauss–Newton (GN) method in order to fully utilize the diversity of temporal–spatial data on local updates. The proposed algorithm includes two steps, i.e., aggregate then update (ATU), where the aggregating step collects in real time the global information instead of local information due to the diffusion strategy, and the updating step implements the local GN iteration. The resulting ATU diffusion algorithm is a distributed and cooperative system without any increase on communication cost, as compared with the noncooperative version. Based on the detailed convergence analysis for ATU, which is fundamental to the promotion of this algorithm, the sufficient conditions for convergence are derived and the evidences of faster convergence than the noncooperative version are provided. The simulation results confirm the obtained theoretical derivations by applying the ATU algorithm to an NLLS-based target localization problem and show the cooperation gains in many aspects, such as the convergence rate, steady-state accuracy, and robustness to noisy range, step size, node, and link failures.

The Internet is nowadays suffering dramatically serious attacks, with the distributed denial of service (DDoS) attacks being the representative and dominant ones. It is seen that, to stabilize the buffer queue length around a given target under DDoS attacks in the relevant routes is vitally important and helpful to mitigate the attacks and to improve the quality of service (QoS) for normal users. In the current paper, a stochastic queue dynamic model with L e ´ vy jump noise, which is affected by the continuous Brownian motion and the discontinuous Poisson process, is worked out to develop a novel and accurate mathematical framework for the stability of a route queue that deals with constant-rate DDoS attacks. This article proposes a security defensive mechanism in the network for solving the network collapse that can possibly be caused by DDoS attacks, otherwise. Particularly, based on the formulation of a stochastic queue dynamic with L e ´ vy jump noise, the mechanism that characterizes the behavior of the queue at routers is presented for stabilizing the queue length under constant-rate DDoS attacks. By applying the stochastic control theory into analyzing the performance of queue dynamic under constant-rate DDoS attacks, some explicit conditions are established under which the instantaneous queue length converges to any given target in a route. Simulation results demonstrate the satisfaction of the proposed defense mechanism with sharp contrast to the state of the art active queue management (AQM) schemes.

We describes four scheduling schemes on the predefined path in large-scale wireless networks supported by full-duplex radios. The end-to-end throughput on multi-hop path with these schemes is discussed, indicating the effect of scheduling schemes and the influence of full-duplex radios on multi-hop data transmission. We make a simulator and compare the throughput results in these schemes. Results show that the new scheduling methods may improve end-to-end throughput on multi-hop path in wireless networks moderately.

Achievable capacity in multi-hop wireless networks is seriously lower than single-hop communication. Two full-duplex nodes potentially have 2× capacity in wireless communications, compared to two half-duplex nodes. Organize the two nodes as one unit and reorganize nodes to be the units in multi-hop paths, the capacity can achieve 1× to 2× under space division simultaneous transmission mode, which is verified by analysis and simulation results.

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 first identifies that there is an issue of cost performance tradeoff, which is stemmed from various limited resources in the IoT systems and the competing services requirements. We second 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 quasiconcave maximization problem. We are able to reduce the original problem to a pseudoconcave 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.

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 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 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. We also propose a simple but effective energy-accuracy tradeoff scheme by using a sigmoidal utility function. 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. The effectiveness of energy–accuracy tradeoff is also validated.

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.

In wireless networks, it is still a challenge to allocate the limited resources among users to meet their specific quality of service (QoS) requirements, especially when the users have different traffic types, i.e., the hard QoS traffic, the best effort traffic, and the soft QoS traffic. In this paper, we develop the utility-based resource allocation algorithms in the following three tasks: 1) resource allocation among the hard QoS traffic and the soft QoS traffic; 2) resource allocation among the best effort traffic and the soft QoS traffic; and 3) finally resource allocation among the hard QoS traffic, the best effort traffic, and the soft QoS traffic, by solving the network utility maximization problem using the Karush-Kuhn-Tucker condition. We develop a number of critical theorems to give the conditions that find the optimal solutions for the above three cases in a unified framework. These theorems then act as design guidelines for the three algorithms. The proposed algorithms take into account the traffic type, the total available resources and the users' channel qualities. We evaluate the time complexity of the proposed algorithms, which comes out to be polynomial, and study the network performance by numerical examples. Numerical results demonstrate the bandwidth allocations, the fairness index and the total maximum utility under different channel qualities and resource situations.

FAST TCP is a promising new transfer control protocol developed for high-speed long-latency networks, whose performance has previously only been studied for data traffic sent in one direction. In this study, the authors propose a mathematical model for bi-directional connections using the FAST TCP protocol, which captures the asymmetric bandwidth dynamics in duplex dumbbell networks, prevalent in ADSL, satellite and other high-speed technologies. Using this model, the authors obtain a powerful result that the queue delays observed by opposite FAST TCP flows only have a time difference in a dumbbell network. Furthermore, the authors establish the conditions under which the bi-directional FAST TCP flows achieve stability, and on this basis the throughput rates of the forward and backward flows at steady-state are deduced. The authors find that, in the case of bandwidth asymmetry and one flow in each direction, in equilibrium the throughput of the bi-directional FAST TCP flows can only achieve the smaller link capacity of the duplex links, and the link with larger capacity is not fully occupied. These theoretical findings are corroborated by NS2 simulations.

It is very useful to infer traffic matrix (TM) from link measurements and routing information, especially for the tasks of capacity planning, traffic engineering and network reliability analysis. This inference problem is ill-posed as it involves more unknowns than data and the challenge lies in this problem is its ill-posed nature. To overcome this challenge, this paper firstly describes the inference problem into an optimization problem, in which the objective is to minimize the Euclidean distance between a certain predetermined prior and the target TM subject to the routing constraints upon the link measurements and TM. We secondly solve this problem by transforming the available routing matrix and applying the Lagrange multipliers method. We therefore propose a novel proposal on inferring TM, termed matrix partitioning and Lagrange multipliers (MPLM). An expression is derived for calculating the TMs from the link measurements and the transformations of the routing matrix. We analyze the computational complexity of MPLM, which comes out to be much less than the state-of-the-art approaches. We also suggest a new method to generate the prior. Numerical results are provided to demonstrate the accuracy of MPLM in estimating TMs, and the on-line TM estimation is also discussed in detail to show the techniques in MPLM.

The network lifetime is a key design factor of mobile ad-hoc networks (MANETs). To prolong the lifetime of MANETs, one is forced to attain the tradeoff of minimizing the energy consumption and load balancing. In MANETs, energy waste resulting from retransmission due to high frame error rate (FER) of wireless channel is significant. In this paper, we propose a novel protocol termed error-aware candidate set routing protocol (ECSRP). ECSRP chooses a route in a candidate subset in the route cache in which all the nodes have enough residual battery power. This approach avoids overusing certain routes. If multiple routes exist in the candidate set, ECSRP employs a metric achieving the tradeoff between energy-efficiency and load balancing to select the optimal route. It also takes channel condition into consideration by incorporating packet loss probability in the computation of energy consumption. This helps to reduce the number of retransmissions and save energy. We evaluate the performance of ECSRP under the Gilbert error model. Simulation results demonstrate that ECSRP outperforms the representative protocol conditional min-max battery cost routing (CMMBCR) protocol in terms of total energy consumption and load balancing.

For mobile ad hoc networks (MANETs) and sensor networks, due to the battery recovery effect, a node with longer idle period after finishing a transmission task will recover more over-consumed energy; however, longer recovery period requires shorter transmission period and hence higher transmission rate which leads to more energy consumption for transmission. Therefore, there is a tradeoff between the energy consumption for transmission and the energy recovery when minimizing the overall energy depletion at each node. This paper firstly defines a new metric termed actual energy depletion (AED), which quantifies the actual energy consumption during a transmission and recovery time unit of a node. We then describe the rate selection problem into an optimizing model to minimize AED subject to the channel's capacity constrain. We propose a novel energy efficient rate selection (EERS) approach for wireless battery operated networks. We analyze the mathematical procedure on how to obtain the required optimal rate for the proposed model and provide its implementation method in real networks in a distributive way. Numerical results are provided to show the efficiency of EERS in prolonging network lifetime.

The application of multi-hop wireless Ad Hoc networks (WANETs) has been facing a great challenge to implement cooperative control over user transmission rates under the resource restrictions. In this paper, we propose an efficient bandwidth allocation scheme called Active-Time based Bandwidth Allocation Scheme (ATBAS) that can ensure fair bandwidth allocation among the users in a multi-hop WANET model. This scheme operates at each hop and fairly allots each competing flow a share of channel time, according to which each hop then computes the updating rate for each flow traversing the hop and records their updated rates into each data packet's special control header. The source of each flow in this scheme can eventually adjust its sending rate to reach its fair share. An algorithm is developed to implement the above mechanism. We will show through simulation results that our proposed algorithm is able to fairly distribute bandwidth among multi-hop flows.

Multipath routing is a key technique to support video transmission over wireless ad hoc networks (WANETs). In WANETs, the lifetime of a node is related to its residual energy, current traffic conditions and the required energy consumed for sending a packet to its next hop in the path. In this paper, we propose a new adaptive routing scheme termed lifetime-aware multipath optimal routing (LAMOR) for supporting high-speed real time video transmission in WANETs, which is optimized in terms of lifetime and analyze its characteristics. Both theoretical analysis and simulation results demonstrate that LAMOR indeed extends network lifetime and improves the transmission quality of video streams

A novel model has been proposed in this paper, it considers a pair of clients as the smallest gene, which was assigned secretion respectively based on proper modification of ant algorithm. We transformed the problem of client selecting server to that of server selecting client. And we adopted GT-ITM as Internet topology generator to generate transit-stub model topology for simulation. In the case of large-scale network topology, the simulation results show that the modified ant algorithm performs better than other heuristic algorithms such as the widest path and the optimized widest path ones.