Multi-Source Temporal Data Aggregation in Wireless Sensor Networks

Data aggregation has been emerged as a basic approach in wireless sensor networks (WSNs) in order to reduce the number of transmissions of sensor nodes.This paper proposes an energy-efficient multi-source temporal data aggregation model called MSTDA in WSNs. In MSTDA model, a feature selection algorithm using particle swarm optimization (PSO) is presented to simplify the historical data source firstly. And then a data prediction algorithm based on improved BP neural network with PSO (PSO-BPNN) is proposed. This MSTDA model, which helps to find out potential laws according to historical data sets, is deployed at both the base station (BS) and the node. Only when the deviation between the actual and the predicted value at the node exceeds a certain threshold, the sampling value and new model are sent to BS. The experiments on the dataset which comes from the actual data collected from 54 sensors deployed in the Intel Berkeley Research lab made a satisfied performance. When the error threshold greater than 0.15, it can decrease more than 80% data transmissions.     

ODMCA: An adaptive data mining control algorithm in multicarrier networks

Multicarrier communication is a promising technique to effectively deliver high data rate and combat delay spread over fading channel, and adaptability is an inherent advantage of multicarrier communication systems. It can be implemented in online data streams. This paper addresses a significant problem in multicarrier networks that arises in data streaming scenarios, namely, today’s data mining is ill-equipped to handle data streams effectively, and pays little attention to the network stability and the fast response [http://www-db.standford.edu/stream]. Furthermore, in analysis of massive data streams, the ability to process the data in a single pass, while using little memory, is crucial. For often the data can be transmitted faster than it can be stored or accessed from disks.To address the question, we present an adaptive control-theoretic explicit rate (ER) online data mining control algorithm (ODMCA) to regulate the sending rate of mined data, which accounts for the main memory occupancies of terminal nodes. This single-pass scheme considers limited memory space to process dynamic data streams, and also explores the adaptive capability, which is employed in a general network computation model for dynamic data streams. The proposed method uses a distributed proportional integrative plus derivative (PID) controller combined with data mining, where the control parameters can be designed to ensure the stability of the control loop in terms of sending rate of mined data. The basic PID approach for the computation network transmission is presented and z-transformation and Schur–Cohn stability test are used to achieve the stability criterion, which ensures the bounded rate allocation without steady state oscillation. We further show how the ODMCA can be used to design a controller, analyze the theoretical aspects of the proposed algorithm and verify its agreement with the simulations in the LAN case and the WAN case. Simulation results show the efficiency of our scheme in terms of high main memory occupancy, fast response of the main memory occupancy and of the controlled sending rates.     

Connectivity and coverage maintenance in wireless sensor networks

One of the main design challenges for wireless sensor networks (WSNs) is to obtain long system lifetime without sacrificing system original performance such as communication connectivity and sensing coverage. A large number of sensor nodes are deployed in redundant fashion in dense sensor networks, which lead to higher energy consumption. We propose a distributed framework for energy efficient connectivity and coverage maintenance in WSNs. In our framework, each sensor makes self-scheduling to separately control the states of RF and sensing unit based on dynamic coordinated reconstruction mechanism. A novel energy-balanced distributed connected dominating set algorithm is presented to make connectivity maintenance; and also a distributed node sensing scheduling is brought forward to maintain the network coverage according to the surveillance requirements. We implemented our framework by C++ programming, and the simulation results show that our framework outperforms several related work by considerably improving the energy performance of sensor networks to effectively extend network lifetime.     

A game-theoretic method of fair resource allocation for cloud computing services

As cloud-based services become more numerous and dynamic, resource provisioning becomes more and more challenging. A QoS constrained resource allocation problem is considered in this paper, in which service demanders intend to solve sophisticated parallel computing problem by requesting the usage of resources across a cloud-based network, and a cost of each computational service depends on the amount of computation. Game theory is used to solve the problem of resource allocation. A practical approximated solution with the following two steps is proposed. First, each participant solves its optimal problem independently, without consideration of the multiplexing of resource assignments. A Binary Integer Programming method is proposed to solve the independent optimization. Second, an evolutionary mechanism is designed, which changes multiplexed strategies of the initial optimal solutions of different participants with minimizing their efficiency losses. The algorithms in the evolutionary mechanism take both optimization and fairness into account. It is demonstrated that Nash equilibrium always exists if the resource allocation game has feasible solutions.     

An anomaly-based detection in ubiquitous network using the equilibrium state of the catastrophe theory

It has been increasingly important for Pervasive and Ubiquitous Applications (PUA) of the network traffic, especially anomaly detection which plays a critical role in enforcing a high protection level of the network against threats. In this paper, we present a network traffic anomaly detection method based on the catastrophe theory. In order to characterize the normal behavior of the network, we construct a profile of the normal network traffic by using an equilibrium surface of the catastrophe theory. When anomalies occur, the state of the network traffic will deviate from the normal equilibrium surface. Then, taking the normal equilibrium surface as a reference, we monitor the ongoing network traffic and we use a new index called as catastrophe distance to quantify the deviation. According to the decision theory, network traffic anomalies can be identified by the catastrophe distance. We evaluate the performance of our approach using the DARPA intrusion detection data set. Experiment results show that our approach is significantly effective on the network traffic anomaly detection.     

不可靠通信环境下无线传感器网络最小能耗广播算法

在实际的通信环境中,由于噪声、报文冲突、信号衰减等因素的影响,无线传感器网络节点间信息交换往往是不可靠的.广播是无线传感器网络中广泛使用的操作,如何在不可靠通信环境下实现能量高效的广播算法,对提高整个无线传感器网络的性能具有重要的理论和应用价值.研究了不可靠通信环境下的无线传感器网络最小能耗广播问题,首先,分析了相邻节点之间最小能耗通信模型,并给出了保证节点接收概率不低于P^*的最优发送半径;然后,讨论了多跳转发策略与节点位置信息之间的关系.在此基础上,提出了一种基于PSO的最小生成树广播算法,通过优化各节点的发送半径,在保证所有节点都能以不低于P^*的概率接收到广播数据包的前提下,实现广播操作的总能耗最小.实验结果表明：所提出的广播算法不仅可使每一个节点的接收概率不小于P^*,而且广播总能耗比改进后的BIP算法要小,具有较好的性能.     

Distributed scheduling algorithms for channel access in TDMA wireless mesh networks

In this paper, we have considered the distributed scheduling problem for channel access in TDMA wireless mesh networks. The problem is to assign time-slot(s) for nodes to access the channels, and it is guaranteed that nodes can communicate with all their one-hop neighbors in the assigned time-slot(s). And, the objective is to minimize the cycle length, i.e., the total number of different time-slots in one scheduling cycle. In single-channel ad hoc networks, the best known result for this problem is proved to be K ² in arbitrary graphs (Chlamtac and Pinter in IEEE Trans. Comput. C-36(6):729–737, 1987) and 25K in unit disk graphs () with K as the maximum node degree. There are multiple channels in wireless mesh networks, and different nodes can use different control channels to reduce congestion on the control channels. In this paper, we have considered two scheduling models for wireless mesh networks. The first model is that each node has two radios, and the scheduling is simultaneously done on the two radios. We have proved that the upper bound of the cycle length in arbitrary graphs can be 2K. The second model is that the time-slots are scheduled for the nodes regardless of the number of radios on them. In this case, we have proved that the upper bound can be (4K−2). We also have proposed greedy algorithms with different criterion. The basic idea of these algorithms is to organize the conflicting nodes by special criterion, such as node identification, node degree, the number of conflicting neighbors, etc. And, a node cannot be assigned to a time-slot(s) until all neighbor nodes, which have higher criterion and might conflict with the current node, are assigned time-slot(s) already. All these algorithms are fully distributed and easy to realize. Simulations are also done to verify the performance of these algorithms.