Lei Shu

Management and applications of trust in Wireless Sensor Networks: A survey

Wireless Sensors Networks (WSNs) are susceptible to many security threats, and because of communication, computation and delay constraints of WSNs, traditional security mechanisms cannot be used. Trust management models have been recently suggested as an effective security mechanism for WSNs. Considerable research has been done on modeling and managing trust. In this paper, we present a detailed survey on various trust models that are geared towards WSNs. Then, we analyze various applications of trust models. They are malicious attack detection, secure routing, secure data aggregation, secure localization and secure node selection. In addition, we categorize various types of malicious attacks against trust models and analyze whether the existing trust models can resist these attacks or not. Finally, based on all the analysis and comparisons, we list several trust best practices that are essential for developing a robust trust model for WSNs.

Green Internet of Things for Smart World

Smart world is envisioned as an era in which objects (e.g., watches, mobile phones, computers, cars, buses, and trains) can automatically and intelligently serve people in a collaborative manner. Paving the way for smart world, Internet of Things (IoT) connects everything in the smart world. Motivated by achieving a sustainable smart world, this paper discusses various technologies and issues regarding green IoT, which further reduces the energy consumption of IoT. Particularly, an overview regarding IoT and green IoT is performed first. Then, the hot green information and communications technologies (ICTs) (e.g., green radio-frequency identification, green wireless sensor network, green cloud computing, green machine to machine, and green data center) enabling green IoT are studied, and general green ICT principles are summarized. Furthermore, the latest developments and future vision about sensor cloud, which is a novel paradigm in green IoT, are reviewed and introduced, respectively. Finally, future research directions and open problems about green IoT are presented. Our work targets to be an enlightening and latest guidance for research with respect to green IoT and smart world.

Sleep Scheduling for Geographic Routing in Duty-Cycled Mobile Sensor Networks

Recently, the research focus on geographic routing, a promising routing scheme in wireless sensor networks (WSNs), is shifting toward duty-cycled WSNs in which sensors are sleep scheduled to reduce energy consumption. However, except the connected-k neighborhood (CKN) sleep scheduling algorithm and the geographic routing oriented sleep scheduling (GSS) algorithm, nearly all research work about geographic routing in duty-cycled WSNs has focused on the geographic forwarding mechanism; further, most of the existing work has ignored the fact that sensors can be mobile. In this paper, we focus on sleep scheduling for geographic routing in duty-cycled WSNs with mobile sensors and propose two geographic-distance-based connected-k neighborhood (GCKN) sleep scheduling algorithms. The first one is the geographic-distance-based connected-kneighborhood for first path (GCKNF) sleep scheduling algorithm. The second one is the geographic-distance-based connected-kneighborhood for all paths (GCKNA) sleep scheduling algorithm. By theoretical analysis and simulations, we show that when there are mobile sensors, geographic routing can achieve much shorter average lengths for the first transmission path explored in WSNs employing GCKNF sleep scheduling and all transmission paths searched in WSNs employing GCKNA sleep scheduling compared with those in WSNs employing CKN and GSS sleep scheduling.

Collaborative Location-Based Sleep Scheduling for Wireless Sensor Networks Integratedwith Mobile Cloud Computing

Recently, much research has proposed to integrate mobile cloud computing (MCC) with wireless sensor networks (WSNs) so that powerful cloud computing can be exploited to process the data gathered by ubiquitous WSNs and share the results with mobile users. However, all current MCC-WSN integration schemes ignore the following two observations: 1) the specific data mobile users request usually depend on the current locations of mobile users 2) most sensors are usually equipped with non-rechargeable batteries with limited energy. In this paper, motivated by these two observations, two novel collaborative location-based sleep scheduling (CLSS) schemes are proposed for WSNs integrated with MCC. Based on the locations of mobile users, CLSS dynamically determines the awake or asleep status of each sensor node to reduce energy consumption of the integrated WSN. Particularly, CLSS1 focuses on maximizing the energy consumption saving of the integrated WSN while CLSS2 considers also the scalability and robustness of the integrated WSN. Theoretical and simulation results show that for WSNs integrated with MCC, both CLSS1 and CLSS2 can prolong the WSN lifetime while still satisfying the data requests of mobile users.

Impacts of Deployment Strategies on Localization Performance in Underwater Acoustic Sensor Networks

When setting up an underwater acoustic sensor network (UASN), node deployment is the first and foremost task, upon which many fundamental network services, such as network topology control, routing, and boundary detection, will be built. While node deployment in 2-D terrestrial wireless sensor networks has been extensively studied, little attention has been received by their 3-D counterparts. This paper aims at analyzing the impacts of node deployment strategies on localization performances in a 3-D environment. More specifically, the simulations conducted in this paper reveal that the regular tetrahedron deployment scheme outperforms the random deployment scheme and the cube deployment scheme in terms of reducing localization error and increasing localization ratio while maintaining the average number of neighboring anchor nodes and network connectivity. Given the fact that random deployment is the primary choice for most of practical applications to date, our results are expected to shed some light on the design of UASNs in the near future.

A Multi-Objective Optimization Scheduling Method Based on the Ant Colony Algorithm in Cloud Computing

For task-scheduling problems in cloud computing, a multi-objective optimization method is proposed here. First, with an aim toward the biodiversity of resources and tasks in cloud computing, we propose a resource cost model that defines the demand of tasks on resources with more details. This model reflects the relationship between the users resource costs and the budget costs. A multi-objective optimization scheduling method has been proposed based on this resource cost model. This method considers the makespan and the users budget costs as constraints of the optimization problem, achieving multi-objective optimization of both performance and cost. An improved ant colony algorithm has been proposed to solve this problem. Two constraint functions were used to evaluate and provide feedback regarding the performance and budget cost. These two constraint functions made the algorithm adjust the quality of the solution in a timely manner based on feedback in order to achieve the optimal solution. Some simulation experiments were designed to evaluate this methods performance using four metrics: 1) the makespan; 2) cost; 3) deadline violation rate; and 4) resource utilization. Experimental results show that based on these four metrics, a multi-objective optimization method is better than other similar methods, especially as it increased 56.6% in the best case scenario.

Two Novel DOA Estimation Approaches for Real-Time Assistant Calibration Systems in Future Vehicle Industrial

Intelligent transportation systems (ITSs) of industrial systems have played an important role in Internet of things (IOT). The assistant calibration system (ACS) of vehicles is an emerging technology, which services the driver to drive the vehicle safely. To solve some existing problems in ACS such as frequency pairing, vehicle localization judgment, and driving in the curve road, two direction-of-arrival (DOA) estimation-based approaches are proposed to resolve these problems. However, the performance of most conventional DOA estimation algorithms is affected by the mutual coupling among the elements. The special structure of the mutual coupling matrix of the uniform linear array is applied to eliminate the effect of mutual coupling. Then, a novel on-grid DOA estimation algorithm based on compressive sensing (CS) strategies is proposed in the presence of unknown mutual coupling. In order to compensate the aperture loss of discarding information that the array receives, the array aperture is extended by the vectorization operator. In order to deal with the effect of grid mismatch, an off-grid DOA estimation algorithm based on sparse Bayesian learning (SBL) is proposed in this paper. The temporal correlation between the neighboring snapshot numbers is considered in the off-grid algorithm. The computer simulation verifies the effectiveness of the proposed algorithms.

A Novel Sensory Data Processing Framework to Integrate Sensor Networks With Mobile Cloud

Taking advantage of the data gathering capability of wireless sensor networks (WSNs) as well as the data storage and processing ability of mobile cloud computing (MCC), WSN-MCC integration is attracting significant attention from both academia and industry. This paper focuses on processing of the sensory data in WSN-MCC integration, by identifying the critical issues concerning WSN-MCC integration and proposing a novel sensory data processing framework, which aims at transmitting desirable sensory data to the mobile users in a fast, reliable, and secure manner. The proposed framework could prolong the WSN lifetime, decrease the storage requirements of the sensors and the WSN gateway, and reduce the traffic load and bandwidth requirement of sensory data transmissions. In addition, the framework is capable of monitoring and predicting the future trend of the sensory data traffic, as well as improving its security. The framework further decreases the storage and processing overhead of the cloud, while enabling mobile users to securely obtain their desired sensory data faster. Analytical and experimental results are presented to demonstrate the effectiveness of the proposed framework.

A Game Theory-Based Energy Management System Using Price Elasticity for Smart Grids

Distributed devices in smart grid systems are decentralized and connected to the power grid through different types of equipment transmit, which will produce numerous energy losses when power flows from one bus to another. One of the most efficient approaches to reduce energy losses is to integrate distributed generations (DGs), mostly renewable energy sources. However, the uncertainty of DG may cause instability issues. Additionally, due to the similar consumption habits of customers, the peak load period of power consumption may cause congestion in the power grid and affect the energy delivery. Energy management with DG regulation is considered to be one of the most efficient solutions for solving these instability issues. In this paper, we consider a power system with both distributed generators and customers, and propose a distributed locational marginal pricing (DLMP)-based unified energy management system (uEMS) model, which, unlike previous works, considers both increasing profit benefits for DGs and increasing stability of the distributed power system (DPS). The model contains two parts: 1) a game theory-based loss reduction allocation (LRA); and 2) a load feedback control (LFC) with price elasticity. In the former component, we develop an iterative loss reduction method using DLMP to remunerate DGs for their participation in energy loss reduction. By using iterative LRA to calculate energy loss reduction, the model accurately rewards DG contribution and offers a fair competitive market. Furthermore, the overall profit of all DGs is maximized by utilizing game theory to calculate an optimal LRA scheme for calculating the distributed loss of every DG in each time slot. In the latter component of the model, we propose an LFC submodel with price elasticity, where a DLMP feedback signal is calculated by customer demand to regulate peak-load value. In uEMS, LFC first determines the DLMP signal of a customer bus by a time-shift load optimization (LO) algorithm based on the changes of customer demand, which is fed back to the DLMP of the customer bus at the next slot-time, allowing for peak-load regulation via price elasticity. Results based on the IEEE 37-bus feeder system show that the proposed uEMS model can increase DG benefits and improve system stability.

A Survey on Deployment Algorithms in Underwater Acoustic Sensor Networks

Node deployment is one of the fundamental tasks for underwater acoustic sensor networks (UASNs) where the deployment strategy supports many fundamental network services, such as network topology control, routing, and boundary detection. Due to the complex deployment environment in three-dimensional (3D) space and unique characteristics of underwater acoustic channel, many factors need to be considered specifically during the deployment of UASNs. Thus, deployment issues in UASNs are significantly different from those of wireless sensor networks (WSNs). Node deployment for UASNs is an attractive research topic upon which a large number of algorithms have been proposed recently. This paper seeks to provide an overview of the most recent advances of deployment algorithms in UASNs while pointing out the open issues. In this paper, the deployment algorithms are classified into three categories based on the mobility of sensor nodes, namely, (I) static deployment, (II) self-adjustment deployment, and (III) movement-assisted deployment. The differences of the representative algorithms in aspects of sensor node types, computation complexity, energy consumption, deployment objectives, and so forth, are discussed and investigated in detail.