Connected P-percent coverage in wireless sensor networks based on degree constraint dominating set approach

In this paper, we propose an algorihm for conneced p-percent coverage probem in Wireless Sensor Networks(WSNs) to improve the over netork life time. In this work, we invstigae the p-pernt coverage problem(PCP) in WSNs which require % of n area should be monitored correctl and to find ou ny additional requirements of the connec p-percent coverge prom. We prose pDCDS algorith which is a learnin autmaton basd algorithm fr PCP pDCDS is a Degreconsained Connected Domining Se based algoithm whch detect the minimum numbe of des to monitor an area. The simulation results demonstrate hat pDCDS can remarkably improve the network lifetime.

Dominating set and network coding-based routing in wireless mesh networks

Wireless mesh networks are widely applied in many fields such as industrial controlling, environmental monitoring, and military operations. Network coding is promising technology that can improve the performance of wireless mesh networks. In particular, network coding is suitable for wireless mesh networks as the fixed backbone of wireless mesh is usually unlimited energy. However, coding collision is a severe problem affecting network performance. To avoid this, routing should be effectively designed with an optimum combination of coding opportunity and coding validity. In this paper, we propose a Connected Dominating Set (CDS)-based and Flow-oriented Coding-aware Routing (CFCR) mechanism to actively increase potential coding opportunities. Our work provides two major contributions. First, it effectively deals with the coding collision problem of flows by introducing the information conformation process, which effectively decreases the failure rate of decoding. Secondly, our routing process considers the benefit of CDS and flow coding simultaneously. Through formalized analysis of the routing parameters, CFCR can choose optimized routing with reliable transmission and small cost. Our evaluation shows CFCR has a lower packet loss ratio and higher throughput than existing methods, such as Adaptive Control of Packet Overhead in XOR Network Coding (ACPO), or Distributed Coding-Aware Routing (DCAR).

Prediction interval-based control of nonlinear systems using neural networks

Prediction interval (PI) is a promising tool for quantifying uncertainties associated with point predictions. Despite its informativeness, the design and deployment of PI-based controller for complex systems is very rare. As a pioneering work, this paper proposes a framework for design and implementation of PI-based controller (PIC) for nonlinear systems. Neural network (NN)-based inverse model within internal model control structure is used to develop the PIC. Firstly, a PI-based model is developed to construct PIs for the system output. This model is then used as an online estimator for PIs. The PIs from this model are fed to the NN inverse model along with other traditional inputs to generate the control signal. The performance of the proposed PIC is examined for two case studies. This includes a nonlinear batch polymerization reactor and a numerical nonlinear plant. Simulation results demonstrated that the proposed PIC tracking performance is better than the traditional NN-based controller.

Market-based sensor relocation by robot team inwireless sensor networks

Randomly scattered sensors may cause sensing holes and redundant sensors. In carrier-based sensor relocation, mobile robots (with limited capacity to carry sensors) pick up additional or redundant sensors and relocate them at sensing holes. In the only known localized algorithm, robots randomly traverse field and act based on identified pair of spare sensor and coverage hole. We propose a Market-based Sensor Relocation (MSR) algorithm, which optimizes sensor deployment location, and introduces bidding and coordinating among neighboring robots. Sensors along the boundary of each hole elect one of them as the representative, which bids to neighboring robots for hole filling service. Each robot randomly explores by applying Least Recently Visited policy. It chooses the best bid according to Cost over Progress ratio and fetches a spare sensor nearby to cover the corresponding sensing hole. Robots within communication range share their tasks to search for better possible solutions. Simulation shows that MSR outperforms the existing competing algorithm G-R3S2 significantly on total robot traversed path and energy, and time to cover holes, slightly on number of sensors needed to cover the hole and number of sensor messages for bidding and deployment location sharing.

A congestion control scheme based on fuzzy logic in wireless body area networks

One of the major challenges in healthcare wireless body area network (WBAN) applications is to control congestion. Unpredictable traffic load, many-to-one communication nature and limited bandwidth occupancy are among major reasons that can cause congestion in such applications. Congestion has negative impacts on the overall network performance such as packet losses, increasing end-to-end delay and wasting energy consumption due to a large number of retransmissions. In life-critical applications, any delay in transmitting vital signals may lead to death of a patient. Therefore, in order to enhance the network quality of service (QoS), developing a solution for congestion estimation and control is imperative. In this paper, we propose a new congestion detection and control protocol for remote monitoring of patients health status using WBANs. The proposed system is able to detect congestion by considering local information such as buffer capacity and node rate. In case of congestion, the proposed system differentiates between vital signals and assigns priorities to them based on their level of importance. As a result, the proposed approach provides a better quality of service for transmitting highly important vital signs.

First-priority relation graph-based malicious users detection in mobile social networks

Mobile social networks (MSNs) consist of many mobile users (individuals) with social characteristics, that provide a variety of data delivery services involving the social relationship among mobile individuals. Because mobile users move around based on their common interests and contact with each other more frequently if they have more social features in common in MSNs. In this paper, we first propose the first-priority relation graph, say FPRG, of MSNs. However, some users in MSNs may be malicious. Malicious users can break the data delivery through terminating the data delivery or tampering with the data. Therefore, malicious users will be detected in the process of looking for the data delivery routing to obtain efficient and reliable data delivery routing along the first-priority relation graph. Secondly, we propose one hamiltonian cycle decomposition of FPRG-based adaptive detection algorithm based on in MSNs under the PMC detection model (the system-level detection model).

Agent-based reactive power management of power distribution networks with distributed energy generation

In this paper, a new agent-based distributed reactive power management scheme is proposed to improve the voltage stability of energy distribution systems with distributed generation units. Three types of agents – distribution system agent, estimator agent, and control agent are developed within the multi-agent framework. The agents simultaneously coordinated their activities through the online information and energy flow. The overall achievement of the proposed scheme depends on the coordination between two tasks – (i) estimation of reactive power using voltage variation formula and (ii) necessary control actions to provide the estimated reactive power to the distribution networks through the distributed static synchronous compensators. A linear quadratic regulator with a proportional integrator is designed for the control agent in order to control the reactive component of the current and the DC voltage of the compensators. The performance of the proposed scheme is tested on a 10-bus power distribution network under various scenarios. The effectiveness is validated by comparing the proposed approach to the conventional proportional integral control approach. It is found that, the agent-based scheme provides excellent robust performance under various operating conditions of the power distribution network.

Wireless video-based sensor networks for surveillance of residential districts

Compared to traditional wired video sensor networks to supervise a residential district, Wireless Video-based Sensor Networks (WVSN) can provide more detail and precise information while reduce the cost. However, state-of-the-art low cost wireless video-based sensors have very constrained resources such as low bandwidth, small storage, limited processing capability, and limited energy resource. Also, due to the special sensing range of video-based sensors, cluster-based routing is not as effective as it apply to traditional sensor networks. This paper provides a novel real-time change mining algorithm based on an extracted profile model of moving objects learnt from frog's eyes. Example analysis shows the extracted profile would not miss any important semantic images to send to the Base Station for further hazards detection, while efficiently reducing futile video stream data to the degree that nowadays wireless video sensor can realize. Thus it makes WVSN available to surveillance of residential districts.

Distributed generation capacity planning for distribution networks to minimize energy loss: an unbalanced multi-phase optimal power flow based approach

This chapter presents an unbalanced multi-phase optimal power flow (UMOPF) based planning approach to determine the optimum capacities of multiple distributed generation units in a distribution network. An adaptive weight particle swarm optimization algorithm is used to find the global optimum solution. To increase the efficiency of the proposed scheme, a co-simulation platform is developed. Since the proposed method is mainly based on the cost optimization, variations in loads and uncertainties within DG units are also taken into account to perform the analysis. An IEEE 123 node distribution system is used as a test distribution network which is unbalanced and multi-phase in nature, for the validation of the proposed scheme. The superiority of the proposed method is investigated through the comparisons of the results obtained that of a Genetic Algorithm based OPF method. This analysis also shows that the DG capacity planning considering annual load and generation uncertainties outperform the traditional well practised peak-load planning.

New inequality-based approach to passivity analysis of neural networks with interval time-varying delay

This paper is concerned with the problem of passivity analysis of neural networks with an interval time-varying delay. Unlike existing results in the literature, the time-delay considered in this paper is subjected to interval time-varying without any restriction on the rate of change. Based on novel refined Jensen inequalities and by constructing an improved Lyapunov-Krasovskii functional (LKF), which fully utilizes information of the neuron activation functions, new delay-dependent conditions that ensure the passivity of the network are derived in terms of tractable linear matrix inequalities (LMIs) which can be effectively solved by various computational tools. The effectiveness and improvement over existing results of the proposed method in this paper are illustrated through numerical examples.

Geospatial estimation-based auto drift correction in wireless sensor networks

Wireless sensor networks are often deployed in large numbers, over a large geographical region, in order to monitor the phenomena of interest. Sensors used in the sensor networks often suffer from random or systematic errors such as drift and bias. Even if they are calibrated at the time of deployment, they tend to drift as time progresses. Consequently, the progressive manual calibration of such a large-scale sensor network becomes impossible in practice. In this article, we address this challenge by proposing a collaborative framework to automatically detect and correct the drift in order to keep the data collected from these networks reliable. We propose a novel scheme that uses geospatial estimation-based interpolation techniques on measurements from neighboring sensors to collaboratively predict the value of phenomenon being observed. The predicted values are then used iteratively to correct the sensor drift by means of a Kalman filter. Our scheme can be implemented in a centralized as well as distributed manner to detect and correct the drift generated in the sensors. For centralized implementation of our scheme, we compare several krigingand nonkriging-based geospatial estimation techniques in combination with the Kalman filter, and show the superiority of the kriging-based methods in detecting and correcting the drift. To demonstrate the applicability of our distributed approach on a real world application scenario, we implement our algorithm on a network consisting of Wireless Sensor Network (WSN) hardware. We further evaluate single as well as multiple drifting sensor scenarios to show the effectiveness of our algorithm for detecting and correcting drift. Further, we address the issue of high power usage for data transmission among neighboring nodes leading to low network lifetime for the distributed approach by proposing two power saving schemes. Moreover, we compare our algorithm with a blind calibration scheme in the literature and demonstrate its superiority in detecting both linear and nonlinear drifts.

Passivity analysis of memristor-based complex-valued neural networks with time-varying delays

In this paper, the model of memristor-based complex-valued neural networks (MCVNNs) with time-varying delays is established and the problem of passivity analysis for MCVNNs is considered and extensively investigated. The analysis in this paper employs results from the theory of differential equations with discontinuous right-hand side as introduced by Filippov. By employing the appropriate Lyapunov–Krasovskii functional, differential inclusion theory and linear matrix inequality (LMI) approach, some new sufficient conditions for the passivity of the given MCVNNs are obtained in terms of both complex-valued and real-value LMIs, which can be easily solved by using standard numerical algorithms. Numerical examples are provided to illustrate the effectiveness of our theoretical results.

Space-crossing : community-based data forwarding in mobile social networks under the hybrid communication architecture

In this paper, we study two tightly coupled issues, space-crossing community detection and its influence on data forwarding in mobile social networks (MSNs). We propose a communication framework containing the hybrid underlying network with access point (AP) support for data forwarding and the base stations for managing most of control traffic. The concept of physical proximity community can be extended to be one across the geographical space, because APs can facilitate the communication among long-distance nodes. Space-crossing communities are obtained by merging some pairs of physical proximity communities. Based on the space-crossing community, we define two cases of node local activity and use them as the input of inner product similarity measurement. We design a novel data forwarding algorithm Social Attraction and Infrastructure Support (SAIS), which applies similarity attraction to route to neighbor more similar to destination, and infrastructure support phase to route the message to other APs within common connected components. We evaluate our SAIS algorithm on real-life datasets from MIT Reality Mining and University of Illinois Movement (UIM). Results show that space-crossing community plays a positive role in data forwarding in MSNs. Based on this new type of community, SAIS achieves a better performance than existing popular social community-based data forwarding algorithms in practice, including Simbet, Bubble Rap and Nguyen's Routing algorithms.

Identifying diffusion sources in large networks: a community structure based approach

The global diffusion of epidemics, rumors and computer viruses causes great damage to our society. It is critical to identify the diffusion sources and promptly quarantine them. However, most methods proposed so far are unsuitable for large networks because of their computational cost and the complex spatiotemporal diffusion processes. In this paper, we develop a community structure based approach to efficiently identify diffusion sources in large networks. We first detect the community structure of a network and assign sensors on community bridge nodes to record diffusion dynamics. From the infection time of bridge sensors, we can determine the very first infected community from which the diffusion started and spread out to other communities. This, therefore, overcomes the scalability issue in source identification problems by narrowing the set of suspects down to the first infected community. Then, to accurately locate the diffusion source from suspects, we utilize an intrinsic feature of diffusion sources that the relative infection time of any node is linear with its effective distance from the diffusion source. Thus, for each suspect, we compute the correlation coefficient to measure the degree of linear dependence between sensors' relative infection times and their effective distances from the suspect, and consider the one with the greatest correlation coefficient as the source. We evaluate our approach in two large networks containing more than 300,000 nodes, which are collected from Twitter. The experiment results show that our method can identify diffusion sources with very high degree of accuracy. Especially when the average community size shrinks, the accuracy of our approach increases dramatically.

Enhancing collaboration in BIM-based construction networks through organisational discontinuity theory: a case study of the new Royal Adelaide Hospital

This paper discusses a case study of Australia’s most technologically advanced health facility to address an identified gap in the body of the knowledge. That is, no comprehensive study has hitherto attempted to draw upon virtual team working theories to enhance collaboration in BIM-based construction networks (BbCNs). In response to this, the present study seeks to raise awareness of organisational discontinuity theory (ODT) as a recent theory for virtual teams, which enables BbCNs to embrace collaboration. To this end, the major challenges encountered and corresponding solutions adopted on a mega-project have been closely monitored and investigated. The study contributes to the field through the conceptualisation of typical barriers to collaboration in BbCNs from the perspective of ODT. Additionally, the discussions presented outline practical implications by demonstrating how the identified issues of collaboration in BbCNs could be effectively dealt with.