Scate: A Scalable Time and Energy Aware Actor Task Allocation Algorithm in Wireless Sensor and Actor Networks

In many applications of wireless sensor actor networks (WSANs) that often run in harsh environments, the reduction of completion times of tasks is highly desired. We present a new time‐aware, energy‐aware, and starvation‐free algorithm called Scate for assigning tasks to actors while satisfying the scalability and distribution requirements of WSANs with semi‐automated architecture. The proposed algorithm allows concurrent executions of any mix of small and large tasks and yet prevents probable starvation of tasks. To achieve this, it estimates the completion times of tasks on each available actor and then takes the remaining energies and the current workloads of these actors into account during task assignment to actors. The results of our experiments with a prototyped implementation of Scate show longer network lifetime, shorter makespan of resulting schedules, and more balanced loads on actors compared to when one of the three well‐known task‐scheduling algorithms, namely, the max‐min, min‐min, and opportunistic load balancing algorithms, is used.     

A Resource Oriented Framework for Service Choreography over Wireless Sensor and Actor Networks

Current Internet of Things (IoT) development requires service distribution over Wireless Sensor and Actor Networks (WSAN) to deal with the drastic increasing of network management complexity. Because of the specific constraints of WSAN, some limitations can be observed in centralized approaches. Multi-hop communication used by WSAN introduces transmission latency, packet errors, router congestion and security issues. As it uses local services, a model of decentralized services avoids long path communications between nodes and applications. But the two main issues are then to design (1) the composition of such services and to map (2) them over the WSAN. This contribution proposes a model for decentralized services based on Resource Oriented Architecture in which their communications are designed thanks to an adaptation of Petri Network (1). In addition, the problem of decentralized service mapping and its deployment over a WSAN is successfully resumed by a Pseudo-Boolean Optimization in order to minimize network communication load (2). These contributions are presented using a proposed EMMA middleware as unifying thread.     

Hybrid simulation of Sensor and Actor Networks with BARAKA

We present BARAKA, a new hybrid simulator for Sensor and Actor Networks (SANETs). This tool provides integrated simulation of communication networks and robotic aspects. It allows the complete modelling of co-operation issues in SANETs including the performance evaluation of either robot actions or networking aspects while considering mutual impact. This hybrid simulation enables new potentials in the evaluation of algorithms developed for communication and co-operation in SANETs. Previously, evaluations in this context were accomplished separately. On the one hand, network simulation helps to measure the efficiency of routing or medium access. On the other hand, robot simulators are used to evaluate the physical movements. Using two different simulators might introduce inconsistent results, and might make the transfer on real hardware harder. With the development of methods and techniques for co-operation in SANETs, the need for integrated evaluation environment increased. To compensate this demand, we developed BARAKA.     

SPINS: Security Protocols for Sensor Networks

Wireless sensor networks will be widely deployed in the near future. While much research has focused on making these networks feasible and useful, security has received little attention. We present a suite of security protocols optimized for sensor networks: SPINS. SPINS has two secure building blocks: SNEP and TESLA. SNEP includes: data confidentiality, two-party data authentication, and evidence of data freshness. TESLA provides authenticated broadcast for severely resource-constrained environments. We implemented the above protocols, and show that they are practical even on minimal hardware: the performance of the protocol suite easily matches the data rate of our network. Additionally, we demonstrate that the suite can be used for building higher level protocols.     

事件驱动的带执行器无线传感器网络分簇算法研究

带有执行器的无线传感器网络是指在传统无线传感器网络中加入执行节点,形成传感器节点、执行节点和基站共同构成的三层监控网络。根据执行器在能量、计算能力和感知能力方面的优势,提出建立应用于事件调度的双环分簇算法。算法将执行器连接成双环结构,提升网络在线扩展能力的同时,也为无线传感器网络满足事件驱动构建基础。仿真实验证明,此算法能够有效降低网络能耗,随着节点数目的增加和监控领域的扩大,表现更加凸出。     

An Adaptive Fuzzy-Based Clustering and Bio-Inspired Energy Efficient Hierarchical Routing Protocol for Wireless Sensor Networks

Wireless Personal Communications - Wireless Sensor Network (WSN) based Communication has been devised for exchanging data with low cost, minimal maintenance, and for more convenience. These...     

pDCS: Security and Privacy Support for Data-Centric Sensor Networks

The demand for efficient data dissemination/access techniques to find relevant data from within a sensor network has led to the development of Data-Centric Sensor (DCS) networks, where the sensor data instead of sensor nodes are named based on attributes such as event type or geographic location. However, saving data inside a network also creates security problems due to the lack of tamper resistance of the sensor nodes and the unattended nature of the sensor network. For example, an attacker may simply locate and compromise the node storing the event of his interest. To address these security problems, we present pDCS, a privacy-enhanced DCS network which offers different levels of data privacy based on different cryptographic keys. pDCS also includes an efficient key management scheme to facilitate the management of multiple types of keys used in the system. In addition, we propose several query optimization techniques based on euclidean Steiner Tree and keyed Bloom Filter (KBF) to minimize the query overhead while preserving query privacy. Finally, detailed analysis and simulations show that the KBF scheme can significantly reduce the message overhead with the same level of query delay and maintain a very high level of query privacy.     

An Enhanced Cluster Head Selection of LEACH Based on Power Consumption and Density of Sensor Nodes in Wireless Sensor Networks

The cluster head of LEACH algorithm was selected randomly in WSNs. It effects the distribution of clusters for the shadowing effect of obstruction in real scenarios. The network consumption is increased because of poor communication between nodes. A new selection method is presented to solve those problems in this paper. The logarithmic function is adopted to eliminate the shadowing effect of obstruction. The most suitable cluster is sorted out because the density of nodes is defined as the new threshold value. Simulation results show that the performances of new algorithm are obviously better than LEACH, ALEACH and Kost-LEACH algorithms. Compared with classical algorithms, the nodes utilization could enhance more than 2.0%, average energy consumption of the nodes is reduced by 9.1 J at least, and the probability of nodes failure to join the cluster could be decreased great than 3.7%.

     

Multibeam Selection for Indoor MIMO Systems: Two Cases of Study

The performance of multiple-input multiple-output (MIMO) systems using beam selection is investigated in this paper. Based on the results of a channel sounding campaign carried out at the University of Manitoba for line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios, it was possible to contrast the results of transmitter, receiver and joint beam selection in rich scattering environments. The channel was characterized in the 1-2.4 GHz frequency band with a multipath delay resolution better than 5.8 ns. The beam selection was performed by exhaustive search method. The results led us to important conclusions regarding the beam selection procedure and its potential to improve the indoor channel capacity. In LOS, the single input single output (SISO) system that favours the maximum power direction of arrival (DOA) maximizes the capacity. Capacity improvements are observed by increasing the number of receiver beams (RBs) only at high signal-to-noise ratios (SNRs) for omnidirectional transmission. The best performance in transmitter beam selection in LOS is observed by increasing the number of transmitter beams (TBs) for high SNRs. In the case of NLOS, the capacity performance is improved when more than a single beam is used in either, transmitter or receiver side. The joint transmitter-receiver beam selection exhibits best capacity performance only for large SNRs in LOS while the SISO systems outperforms any joint beam selection alternative for low SNRs. In contrast, in NLOS environments, the use of joint beam selection shows a constant capacity performance improvement starting from lower SNR than in the LOS case     

无线传感执行器网络双射频通信节点设计与实现

针对无线传感器执行器网络拓扑结构动态变化、通信量大、实时性强及协同工作等特点,同时为降低通信时的信号干扰,设计并实现了一种基于IEEE 802.15.4的双射频多信道通信节点。对关键的天线电路进行了分析和设计;开发了通信模块与控制决策中心的USART接口程序和双通信芯片间的I2C接口程序。     

RaCH: A New Radial Cluster Head Selection Algorithm for Wireless Sensor Networks

Wireless Personal Communications - This paper presents a radial cluster heads selection mechanism for homogenous wireless sensor networks. It aims to ensure a good load balancing and enhance the...     

A Security Model Based on Relational Model for Semantic Sensor Networks

This paper proposes a novel security model for secure query processing in semantic sensor networks. A semantic sensor network (SSN) is a sensor network including semantics of sensory data and context information, and relationships between the semantics by using Semantic Web technologies. Even though much research has been activated on SSN, there is little activity on how to securely access data in semantic sensor networks. Most of storages have been developed based on relational database model and the relational database model provides a secure and robust security support. Therefore, we need to devise a security model considering such a real environment. This paper proposes a new access control model for secure query processing in semantic sensor networks. The proposed security model is based on relational database security model. This paper shows the overall framework and definitions of the proposal, and the experiment and evaluation is described to show validity of our proposal. With the experiment and evaluation, it is clear that the proposed model provides a secure access control support for SSNs.     

无线传感器网络路由协议安全性研究

无线传感器网络的特性使它面临着比传统网络更大的安全挑战。路由协议作为无线传感器网络的关键因素,其安全更为重要。介绍了无线传感器网络路由协议分类及其脆弱性,分析了几种网络路由协议的攻击方法,阐述了网络路由协议的安全策略。     

一种基于位置的WSAN密钥预分配方案

论文从无线传感器和执行器网络的信息传输方式出发,提出一种新的密钥预分配方案。方案充分利用执行器节点具有能量充足、存储器资源丰富、较强计算和通信能力等特点,结合部署位置信息,在执行器节点-执行器节点和执行器-传感器节点两个通信层上采用不同的密钥预分配方案。通过分析可知,新方案在连通性、安全性、计算和通信消耗上都有良好性能。     

Joint Nodes and Sink Mobility Based Immune Routing-Clustering Protocol for Wireless Sensor Networks

Wireless Personal Communications - Recently, mobile wireless sensor network has drawn attention widely. In this paper, Joint Nodes and Sink Mobility based Immune routing-Clustering protocol...     

无线传感器网络可靠的传输层协议安全性研究

端到端通信的可靠性是无线传感器网络许多应用场合的重要条件。对无线传感器网络传输层安全机制进行了综合分析,在建立攻击模型的基础上,深入研究了无线传感器网络可靠的传输层协议安全行问题,并具体提出了安全防御方法,为进一步的研究拓展了思路。     

Q-learning Reward Propagation Method for Reducing the Transmission Power of Sensor Nodes in Wireless Sensor Networks

In wireless sensor networks (WSNs), sensor nodes and sink nodes communicate among themselves to collect and send data. Because of the volume of data transmission, it is important to minimize the power used for this communication. Q-learning can be applied to find the optimal path between two nodes. However, Q-learning suffers from having a significant learning time, meaning that the learning process must be conducted in advance in order to be applicable to WSNs. Many studies have proposed methods to decrease the learning time by reducing the state spaces and updating more Q-values at each update. Reducing the size of the Q-learning state space leads to an inability to execute the optimum action, because the correct state may not be available. Other methods utilize additional information by involving a teacher, control the time flow of Q-learning by considering the number of updates for each Q-value, or use a prioritized queue in the update procedure. Such methods are not well suited to the real-world environment and complexity of WSNs. A more suitable method involves updating the Q-values iteratively. A combination of these updating methods may enhance the reduction in learning time. This paper proposes a reward propagation method (RPM), i.e., a method that integrates various updating algorithms to propagate the reward of the goal state to more Q-values, thus reducing the learning time required for Q-learning. By not only updating the Q-value of the last visited state and executed action, but also updating the Q-values of unvisited states and unexecuted actions, the learning time can be reduced considerably. In this method, we integrate the following three Q-value updating algorithms. First, we incorporate the concept of $\text{ Q }(\lambda )$ -learning. This method iteratively propagates the terminal reward to the Q-values of the visited states until the terminal reward is received. If the terminal reward were to be propagated to Q-values of unvisited states, the learning time could be further reduced. Second, the concept of reward propagation is expanded. The previous, one-step reward update method updates any Q-values of states in which the terminal reward can be received by executing only one action after receiving the terminal reward. If the terminal reward is propagated to states for which the reward will only be received by executing more than one action, more Q-values can be updated. Third, we apply a type of fuzzy Q-learning with eligibility, which updates the Q-values of unexecuted actions. Even though this method is not utilized directly, the concept of updating the Q-values of unexecuted actions is applied. To investigate how much learning time is reduced with using the proposed method, we compare it with conventional Q-learning. Given that the optimal path problem of WSNs can be remodeled as a reinforcement learning problem, a hunter–prey capture game is utilized, which involves two agents in a grid environment. RPM, which plays the role of prey, learns to receive the terminal reward and escape from the hunter. The hunter selects one of two movement policies randomly, and executes one action based on the selected policy. To measure the difference between the success rates of conventional Q-learning and RPM, an equivalent environment and parameters are used for both these methods. We conduct three experiments: the first to compare RPM and conventional Q-learning, the second to test the scalability of RPM, and the third to evaluate the performance of RPM in a differently configured environment. The RPM results are compared with those of conventional Q-learning in terms of the success rate of receiving the terminal reward. This provides a measure of the difference in required learning times. The greatest reduction in learning time is obtained with a grid size of $10 \times 10$ , no obstacles, and 3,000 episodes to be learned. In these episodes, the success rate of RPM is 232 % higher than that of conventional Q-learning. We perform two experiments to verify the scalability of RPM by changing the size of the environment. In a $12 \times 12$ grid environment, RPM initially exhibits a maximum success rate 176 % higher than that of conventional Q-learning. However, as the size of the environment is increased, the effect of propagating the terminal rewards decreases, and the improvement in the success rate compared to conventional Q-learning decreases relative to the $10 \times 10$ grid environment. With a $14 \times 14$ grid environment, the relative effect of RPM declines further, giving a maximum success rate that is around 138 % higher than that of conventional Q-learning. The results of the scalability experiments show that increasing the size of the environment without changing the scope of terminal reward propagation causes a decrease in the success rate of RPM. However, RPM still exhibits a higher success rate than conventional Q-learning. The improvement in the peak success rate of between 138 % and 232 % can greatly reduce the learning time in difficult environments. If the amount of the calculation of terminal reward propagation is not a critical issue, given that the calculation amount is increased exponentially in proportion to the scope of terminal reward propagation, the learning time can be reduced further easily by increasing the scope. Finally, we compare the difference in success rates when obstacles are deployed within the $10 \times 10$ environment. The obstacles naturally degraded the performance of both RPM and conventional Q-learning, but the proposed method still outperforms conventional Q-learning by about 20 % to 59 %. Our experimental results show that the peak success rate of the proposed method is consistently superior to that of conventional Q-learning. Given that the size of environment and the number of obstacles effects the improvement of RPM comparing to conventional Q-learning, the improvement is in proportion to the number of more updated Q-values. If the size of environment is increased, the rate how much Q-values are more updated comparing to whole Q-values is decreased. Therefore, the effect of RPM is also decreased. More the number of obstacles is increased, less the number of Q-values are update, which also reduces the effect of RPM. As the learning time can be reduced by RPM, Q-learning can be applied to diverse fields in which the learning time problem affects its application to WSNs. Although a lot of researches of Q-learning are proposed to solve the learning time problem of Q-learning, the demand of reducing the learning time is still urgent. Given that one line of researches that reduce learning time by updating Q-value more is very active topic, we expect further expansion by combining more kinds of concepts to reduce learning time on RPM.     

基于相邻区域协作的轻量级无线传感器网络安全认证协议

为资源受限的无线传感器网络节点提供秘钥认证方案是一项具有挑战性的工作。文章提出了一种轻量级的基于相邻区域协作的无线传感器网络安全认证协议,采用对称密钥加密技术以及密钥预分配的策略。每个节点只需要发送两条广播信息,即可完成对网络的密钥分配任务从而达到高效节能的目的。通过与其他传统的安全认证协议进行比较分析可以看出,我们的秘钥分配方案在安全认证、抵御重放攻击以及节能等方面更具优势。     

“激活安全”—信息安全网络的新策略

随着信息系统和网络的飞速发展,特别是因特网和电子商务的广泛应用,网络安全甚或至于信息安全就成为一个主要问题。这就必须开发出一种能够自动地检测并响应新安全威胁的新安全策略。重点介绍了“激活安全”及其基础设施的新概念和主要功能,这种“激活安全”系统由多个分系统组成,其中包括入侵检测系统、易损性评估检测装置、防火墙和其他安全部件,这些部件能够互通有关信息,并对各种安全威胁作出响应。还给出了“激活安全”系统的两个应用实例。     

基于ZigBee的煤矿综合监控系统节点设计

针对目前有线煤矿安全监控系统存在网络布线麻烦、节点数量有限、安装不够灵活、组网不便等诸多弊端,设计了基于ZigBee的煤矿综合监控系统传感器节点,该节点能监测井下多种环境信息,可实现人员的大致定位,与监控软件配合,能够自动入网、自动组态。同时,根据煤矿实际情况,制定了一套专用通信协议,在保证数据传输可靠的前提下支持节点上述功能,实际运行证明,该节点工作稳定可靠。