A probabilistic and opportunistic flooding algorithm in wireless sensor networks

In wireless sensor networks, many communication protocols and applications rely on flooding for various networking purposes. Prior efforts focus on how to design efficient flooding algorithms; that is, they seek to achieve full reliability while reducing the number of redundant broadcasting across the network. To achieve efficient flooding, most of the existing protocols try to reduce the number of transmissions, which is decided without considering any online transmission result. In this paper, we propose a probabilistic and opportunistic flooding algorithm that controls rebroadcasts and retransmissions opportunistically. It seeks to achieve a target reliability required by an application. For this purpose, it makes a given node select only the subset of its one-hop neighbors to rebroadcast the same message. It considers node relations such as link error rates among nodes in selecting eligible neighbors to rebroadcast. The sender controls the number of retransmissions opportunistically by tracking the current status of message reception at its neighbors. Simulation is carried out to reveal that our proposed scheme achieves the given target reliability with less overhead than other flooding algorithms in most cases, thus prolonging the network lifetime.     

pth moment stability analysis of stochastic recurrent neural networks with time-varying delays

This paper addresses, in great detail, the issue of pth moment exponential stability of stochastic recurrent neural networks with time-varying delays. With the help of the Dini-derivative of the expectation of V(t,X(t)) “along” the solution X(t) of the model and the technique of Halanay-type inequality, some novel sufficient conditions on pth moment exponential stability of the trivial solution has been established. Results of the development as presented in this paper are more general than those reported in some previously published papers. An example is also given to illustrate that our results are correct and effectiveness.     

The value of information in stochastic maximum flow problems

In this paper, we study the maximum flow problem in stochastic networks with random arc failures. We present the concept of expected value of a given flow and seek a flow whose expected value is maximum. We also introduce the concept of expected capacity of a given cut. While the expected capacity of a cut can be computed in polynomial time, we show that it is NP-hard to compute the expected value of a flow.     

A study on the redundancy of flooding in unstructured p2p networks

In this study we consider flooding, a fundamental mechanism for network discovery and query routing, in unstructured peer-to-peer networks. Flooding has well-known properties such as fast responses and quick network coverage but at the same time it suffers from high overheads due to unnecessarily generated traffic (duplicate messages). Although there has been a significant amount of research on strategies that try to moderate this drawback, there has been no work that aims at quantifying it. This is the subject of this paper; we analyse the behaviour of flooding related to duplicate messages and provide simple bounds and approximate models to assess the associated overheads.     

Ultimate boundedness of stochastic Hopfield neural networks with time-varying delays

By employing Lyapunov functional theory as well as linear matrix inequalities, ultimate boundedness of stochastic Hopfield neural networks (HNN) with time-varying delays is investigated. Sufficient criteria on ultimate boundedness of stochastic HNN are firstly obtained, which fills up a gap and includes deterministic systems as our special case. Finally, numerical simulations are presented to illustrate the correctness and effectiveness of our theoretical results.     

Finite-time robust stochastic stability of uncertain stochastic delayed reaction-diffusion genetic regulatory networks

This paper is concerned with the problem of finite-time stability analysis for uncertain stochastic delayed reaction-diffusion genetic regulatory networks. The parameter uncertainties are assumed to be norm-bounded, and the time delays are assumed to be time-varying. Based on the Lyapunov functional method, sufficient conditions ensuring the networks to be finite-time robustly stochastically stable are established. When there are no norm-bounded parameter uncertainties in the networks, a finite-time stochastic stability condition is also established. All the conditions are diffusion-dependent as well as delay-dependent. Numerical examples are given to illustrate the effectiveness of the proposed results.     

Least possible time paths in stochastic, time-varying networks

In this paper, two computationally efficient algorithms are presented for determining the least possible time paths for all origins to a single destination in networks where the arc weights are discrete random variables whose probability distribution functions vary with time. The first algorithm determines the least possible time path from each node for each departure time interval, the least possible travel time and a lower bound on the associated probability of the occurrence of this travel time. The second algorithm determines up to k least possible time paths, the associated travel times and the corresponding probabilities of occurrence of the travel times (or a lower bound on this probability). No such efficient algorithms for determining least time paths in stochastic, time-varying networks exist in the literature.     

Complexity and stochastic evolution of dyadic networks

A strategic model of network formation is developed which permits unreliable links and organizational costs. Finding a connected Nash network which guarantees a given payoff to each player proves to be an NP-hard problem. For the associated evolutionary game with asynchronous updating and logit updating rules, the stochastically stable networks are characterized.     

Anti-synchronization of stochastic perturbed delayed chaotic neural networks

This paper studies the anti-synchronization of a class of stochastic perturbed chaotic delayed neural networks. By employing the Lyapunov functional method combined with the stochastic analysis as well as the feedback control technique, several sufficient conditions are established that guarantee the mean square exponential anti-synchronization of two identical delayed networks with stochastic disturbances. These sufficient conditions, which are expressed in terms of linear matrix inequalities (LMIs), can be solved efficiently by the LMI toolbox in Matlab. Two numerical examples are exploited to demonstrate the feasibility and applicability of the proposed synchronization approaches.     

Passivity analysis of stochastic delayed neural networks with Markovian switching

In this paper, the problem of passivity analysis is investigated for a class of stochastic delayed neural networks with Markovian switching. By applying Lyapunov functional and free-weighting matrix, delay-dependent/independent passivity criteria are presented in terms of linear matrix inequalities. The results herein include existing ones for neural networks without Markovian switching as special cases. An example is given to demonstrate the effectiveness of the proposed criteria.     

The balance of routing energy consumption in wireless sensor networks

In order to tackle the energy hole problem of sensor networks, the non-uniform node deployment strategy was presented recently. For achieving the expected performance of this deployment method, nodes need to transmit data to the sink node by selecting a node in the adjacent inner region decided by the deployment strategy. Since nodes near the outer boundary of a region will be covered by more nodes, the random selection method will cause the unbalanced energy consumption problem. In this paper, this issue is rigorously studied and a region constraint selection scheme is proposed based on the analytical result. By combining the region constraint strategy and the maximum energy node selection mechanism, a hybrid scheme is presented. Numerical and simulation results show that the region constraint scheme can achieve acceptable performance improvements over the random scheme and the hybrid mechanism also gains better performance in comparison to the maximum energy node selection scheme.     

Mixing local and global information for community detection in large networks

Clustering networks play a key role in many scientific fields, from Biology to Sociology and Computer Science. Some clustering approaches are called global because they exploit knowledge about the whole network topology. Vice versa, so-called local methods require only a partial knowledge of the network topology. Global approaches yield accurate results but do not scale well on large networks; local approaches, vice versa, are less accurate but computationally fast. We propose CONCLUDE (COmplex Network CLUster DEtection), a new clustering method that couples the accuracy of global approaches with the scalability of local methods. CONCLUDE generates random, non-backtracking walks of finite length to compute the importance of each edge in keeping the network connected, i.e., its edge centrality. Edge centralities allow for mapping vertices onto points of a Euclidean space and compute all-pairs distances between vertices; those distances are then used to partition the network into clusters.     

Cluster synchronization in an array of coupled stochastic delayed neural networks via pinning control

In this paper, cluster synchronization problem is studied for an array of coupled stochastic delayed neural networks by using pinning control strategy. Based on the free matrix approach and stochastic analysis techniques, some sufficient criteria are derived to ensure cluster synchronization of the network model if a single linear or adaptive feedback controller is added to each cluster. Furthermore, two specific methods are given to achieve desired cluster synchronization pattern. Finally, a numerical example is provided to demonstrate the effectiveness of the obtained theoretical results.     

Exponential stability of hybrid stochastic neural networks with mixed time delays and nonlinearity

This paper is concerned with the problem of robust exponential stability for a class of hybrid stochastic neural networks with mixed time-delays and Markovian jumping parameters. In this paper, free-weighting matrices are employed to express the relationship between the terms in the Leibniz–Newton formula. Based on the relationship, a linear matrix inequality (LMI) approach is developed to establish the desired sufficient conditions for the mixed time-delays neural networks with Markovian jumping parameters. Finally, two simulation examples are provided to demonstrate the effectiveness of the results developed.     

Mean square exponential stability of stochastic genetic regulatory networks with time-varying delays

In this paper, we study the mean square exponential stability of stochastic genetic regulatory networks with time-varying delays. Two kinds of time-varying delays are considered: one is differentiable with bounded delay derivative the other is continuous without constraints on the delay derivative. In order to investigate the mean square exponential stability in stochastic genetic regulatory networks, some novel rate-dependent/independent mean square exponential stability criteria are derived by constructing Lyapunov-Krasovskii functional. The sufficient conditions are given in terms of linear matrix inequalities. Moreover, illustrative examples are used to substantiate the effectiveness and less conservativeness of our results.     

Delay-distribution-dependent stability of stochastic discrete-time neural networks with randomly mixed time-varying delays

In this paper, the stability analysis problem for a new class of discrete-time neural networks with randomly discrete and distributed time-varying delays has been investigated. Compared with the previous work, the distributed delay is assumed to be time-varying. Moreover, the effects of both variation range and probability distribution of mixed time-delays are taken into consideration in the proposed approach. The distributed time-varying delays and coupling term in complex networks are considered by introducing two Bernoulli stochastic variables. By using some novel analysis techniques and Lyapunov–Krasovskii function, some delay-distribution-dependent conditions are derived to ensure that the discrete-time complex network with randomly coupling term and distributed time-varying delay is synchronized in mean square. A numerical example is provided to demonstrate the effectiveness and the applicability of the proposed method.     

Time-optimized contextual information forwarding in mobile sensor networks

We study on the forwarding of quality contextual information in mobile sensor networks (MSNs). Mobile nodes form ad-hoc distributed processing networks that produce accessible and quality-stamped information about the surrounding environment. Due to the dynamic network topology of such networks the context quality indicators seen by the nodes vary over time. A node delays the context forwarding decision until context of better quality is attained. Moreover, nodes have limited resources, thus, they have to balance between energy conservation and quality of context. We propose a time-optimized, distributed decision making model for forwarding context in a MSN based on the theory of optimal stopping. We compare our findings with certain context forwarding schemes found in the literature and pinpoint the advantages of the proposed model.     

Further results for global exponential stability of stochastic memristor-based neural networks with time-varying delays

In recent years, the stability problems of memristor-based neural networks have been studied extensively. This paper not only takes the unavoidable noise into consideration but also investigates the global exponential stability of stochastic memristor-based neural networks with time-varying delays. The obtained criteria are essentially new and complement previously known ones, which can be easily validated with the parameters of system itself. In addition, the study of the nonlinear dynamics for the addressed neural networks may be helpful in qualitative analysis for general stochastic systems. Finally, two numerical examples are provided to substantiate our results.