Energy harvesting,cooperative spectrum sensing,and data transmission improvement in energy‐efficient multichannel cognitive sensor networks

In this paper, we investigate the energy harvesting capability in a multichannel wireless cognitive sensor networks for energy‐efficient cooperative spectrum sensing and data transmission. Spectrum sensors can cooperatively scan the spectrum for available channels, whereas data sensors transmit data to the fusion center (FC) over those channels. We select the sensing, data transmission, and harvesting sensors by setting the sensing time, data transmission time, and also harvesting time to maximize the network data transmission rate and improve the total energy consumption in the multichannel network under global probability of false alarm and global probability of detection constraints. We formulate our optimization problem and employ the convex optimization method to obtain the optimal times and nodes for spectrum sensing, data transmission, and harvesting energy in each subchannel for multiband cognitive sensor networks. Simulation results show that in our proposed algorithm, the network data transmission rate is improved while more energy is saved compared with the baseline methods with equal sensing time in all subchannels.     

Connectivity properties of large-scale sensor networks

In wireless sensor networks, both nodes and links are prone to failures. In this paper we study connectivity properties of large-scale wireless sensor networks and discuss their implicit effect on routing algorithms and network reliability. We assume a network model of n sensors which are distributed randomly over a field based on a given distribution function. The sensors may be unreliable with a probability distribution, which possibly depends on n and the location of sensors. Two active sensor nodes are connected with probability p _e (n) if they are within communication range of each other. We prove a general result relating unreliable sensor networks to reliable networks. We investigate different graph theoretic properties of sensor networks such as k-connectivity and the existence of the giant component. While connectivity (i.e. k = 1) insures that all nodes can communicate with each other, k-connectivity for k > 1 is required for multi-path routing. We analyze the average shortest path of the k paths from a node in the sensing field back to a base station. It is found that the lengths of these multiple paths in a k-connected network are all close to the shortest path. These results are shown through graph theoretical derivations and are also verified through simulations.     

Connectivity maintenance and coverage preservation in wireless sensor networks

In wireless sensor networks, one of the main design challenges is to save severely constrained energy resources and obtain long system lifetime. Low cost of sensors enables us to randomly deploy a large number of sensor nodes. Thus, a potential approach to solve lifetime problem arises. That is to let sensors work alternatively by identifying redundant nodes in high-density networks and assigning them an off-duty operation mode that has lower energy consumption than the normal on-duty mode. In a single wireless sensor network, sensors are performing two operations: sensing and communication. Therefore, there might exist two kinds of redundancy in the network. Most of the previous work addressed only one kind of redundancy: sensing or communication alone. Wang et al. [Intergrated Coverage and Connectivity Configuration in Wireless Sensor Networks, in: Proceedings of the First ACM Conference on Embedded Networked Sensor Systems (SenSys 2003), Los Angeles, November 2003] and Zhang and Hou [Maintaining Sensing Coverage and Connectivity in Large Sensor Networks. Technical report UIUCDCS-R-2003-2351, June 2003] first discussed how to combine consideration of coverage and connectivity maintenance in a single activity scheduling. They provided a sufficient condition for safe scheduling integration in those fully covered networks. However, random node deployment often makes initial sensing holes inside the deployed area inevitable even in an extremely high-density network. Therefore, in this paper, we enhance their work to support general wireless sensor networks by proving another conclusion: “the communication range is twice of the sensing range” is the sufficient condition and the tight lower bound to ensure that complete coverage preservation implies connectivity among active nodes if the original network topology (consisting of all the deployed nodes) is connected. Also, we extend the result to k-degree network connectivity and k-degree coverage preservation.     

Extremal properties of three-dimensional sensor networks with applications

We analyze various critical transmitting/sensing ranges for connectivity and coverage in three-dimensional sensor networks. As in other large-scale complex systems, many global parameters of sensor networks undergo phase transitions. For a given property of the network, there is a critical threshold, corresponding to the minimum amount of the communication effort or power expenditure by individual nodes, above (respectively, below) which the property exists with high (respectively, a low) probability. For sensor networks, properties of interest include simple and multiple degrees of connectivity/coverage. First, we investigate the network topology according to the region of deployment, the number of deployed sensors, and their transmitting/sensing ranges. More specifically, we consider the following problems: assume that n nodes, each capable of sensing events within a radius of r, are randomly and uniformly distributed in a 3-dimensional region R of volume V, how large must the sensing range R/sub SENSE/ be to ensure a given degree of coverage of the region to monitor? For a given transmission range R/sub TRANS/, what is the minimum (respectively, maximum) degree of the network? What is then the typical hop diameter of the underlying network? Next, we show how these results affect algorithmic aspects of the network by designing specific distributed protocols for sensor networks.     

CWSC: Connected k-coverage working sets construction algorithm in wireless sensor networks

One of the most important issues for wireless sensor networks is to get a long network lifetime without affecting either communication connectivity or sensing coverage. Many sensors that are deployed randomly in a dense sensor network in a redundant way waste a lot of energy. One effective way to save energy is to let only a subset of sensors work at any given time. In this paper, we mainly consider such a problem. Selecting the minimum number of connected sensor nodes that can provide k-coverage (k ≥ 1), i.e., selecting a subset S of working sensors, such that almost every point in the sensing region can be covered by at least k sensors and the sensors in S can form a connected communication subgraph. We propose a connected k-coverage working sets construction algorithm (CWSC) based on Euclidean distance to k-cover the sensing region while minimizing the number of working sensors. CWSC can produce different coverage degrees according to different applications, which can enhance the flexibility of the sensor network. Simulation results show that the proposed algorithm, which can conserve energy and prolong the lifetime of the sensor network, is better than the previous algorithms.     

Some Fundamental Results on Complex Network Problem for Large-Scale Wireless Sensor Networks

Wireless sensor networks (WSNs) are typically constituted by a large number of connected wireless sensors (nodes), generally distributed at random on a given surface area. In such large-scale networks, the desired global system performance is achieved by gathering local information and decisions collected from each individual node. There exist two fundamental global issues on WSNs that we consider here, i.e. full network connectivity and network lifetime. Full connectivity can be obtained either by increasing transmission range, at the expense of consuming higher transmission power, or by increasing the number of sensors, i.e. by increasing network costs. Both of them are closely related to global network lifetime, in the sense that the higher the power consumption or the more sensors deployed the shorter the network lifetime [31]. So the main question is, how can one design large-scale random networks in order to have both global connectivity and maximum network lifetime? Although these questions have been addressed often in the past, a definite, simple predicting algorithm for achieving these goals does not exist so far. In this paper, we aim to discuss such a scheme and confront it with extensive simulations of random networks generated numerically. Specifically, we study the minimum number of nodes required to achieve full network connectivity, and present an analytical formula for estimating it. The results are in very good agreement with the numerical simulations as a function of transmission range. In addition, we study in detail several other statistical properties of large-scale WSNs, such as average path distance, clustering coefficient, degree distribution, etc., also as a function of the transmission range, both qualitatively and quantitatively. We discuss results on how to further improve network energy consumption from the original networks considered by switching off (deleting) some nodes at random but keeping whole network connectivity. The present results are expected to be useful for the design of more efficient large-scale WSNs.     

A Learning Automata Based Area Coverage Algorithm for Wireless Sensor Networks

One way to reduce energy consumption in wireless sensor networks is to reduce the number of active nodes in the network. When sensors are redundantly deployed, a subset of sensors should be selected to actively monitor the field （referred to as a ＂cover＂）, whereas the rest of the sensors should be put to sleep to conserve their batteries. In this paper, a learning automata based algorithm for energy-efficient monitoring in wireless sensor networks （EEMLA） is proposed. Each node in EEMLA algorithm is equipped with a learning automaton which decides for the node to be active or not at any time during the operation of the network. Using feedback received from neighboring nodes, each node gradually learns its proper state during the operation of the network. Experimental results have shown that the proposed monitoring algorithm in comparison to other existing methods such as Tian and LUC can better prolong the network lifetime.     

Impact of Interference on Coverage in Wireless Sensor Networks

The quality of surveillance is dependent on the sensing coverage of a wireless sensor network. In the present paper, we examine how interference affects the coverage of a wireless sensor network. The coverage fraction and required number of sensors for randomly deployed and well-planned deployed wireless sensor networks in the presence of interferers are computed. The required number of sensors to achieve higher level of coverage increases drastically for randomly distributed sensor nodes where the interference effect is high. In the case of well-planned distributed sensor network, required sensors increases linearly as interference effects become more pronounced. Algorithms for computing the required number of sensors to obtain the desired level of coverage in the presence of non-uniform interference is presented. The simulation results suggest that the coverage per subregion and coverage per sensor approaches towards, the improvement achieved is constant. The sensor saving ratio is independent of the level of the desired coverage provided the coverage per subregion is larger than or equal to the coverage per sensor.     

Exploiting Reactive Mobility for Collaborative Target Detection in Wireless Sensor Networks

Recent years have witnessed the deployments of wireless sensor networks in a class of mission-critical applications such as object detection and tracking. These applications often impose stringent Quality-of-Service requirements including high detection probability, low false alarm rate, and bounded detection delay. Although a dense all-static network may initially meet these Quality-of-Service requirements, it does not adapt to unpredictable dynamics in network conditions (e.g., coverage holes caused by death of nodes) or physical environments (e.g., changed spatial distribution of events). This paper exploits reactive mobility to improve the target detection performance of wireless sensor networks. In our approach, mobile sensors collaborate with static sensors and move reactively to achieve the required detection performance. Specifically, mobile sensors initially remain stationary and are directed to move toward a possible target only when a detection consensus is reached by a group of sensors. The accuracy of final detection result is then improved as the measurements of mobile sensors have higher Signal-to-Noise Ratios after the movement. We develop a sensor movement scheduling algorithm that achieves near-optimal system detection performance under a given detection delay bound. The effectiveness of our approach is validated by extensive simulations using the real data traces collected by 23 sensor nodes.     

Modeling Pairwise Key Establishment for Random Key Predistribution in Large-Scale Sensor Networks

Sensor networks are composed of a large number of low power sensor devices. For secure communication among sensors, secret keys are required to be established between them. Considering the storage limitations and the lack of post-deployment configuration information of sensors, random key predistribution schemes have been proposed. Due to limited number of keys, sensors can only share keys with a subset of the neighboring sensors. Sensors then use these neighbors to establish pairwise keys with the remaining neighbors. In order to study the communication overhead incurred due to pairwise key establishment, we derive probability models to design and analyze pairwise key establishment schemes for large-scale sensor networks. Our model applies the binomial distribution and a modified binomial distribution and analyzes the key path length in a hop-by-hop fashion. We also validate our models through a systematic validation procedure. We then show the robustness of our results and illustrate how our models can be used for addressing sensor network design problems.     

Anycast tree-based routing in mobile wireless sensor networks with multiple sinks

A routing scheme for wireless sensor networks with mobile sensors and mobile multiple sinks is proposed and studied. The scheme is based on expanding ring search, anycast messaging and reactive mode with maintaining route state information in sensors. As a result of a successful routing request issued by the sensor, it becomes a member of a routing tree with some sink as a root. Anycast messaging is used only at the stage of establishing a path from a sensor to a sink. Replies from sinks are always forwarded in unicast mode. This considerably reduces network traffic and, as a result, energy consumption by sensors. To take into account routing conditions for network nodes in receiving messages from different directions, the receiving area of each node is assumed to consist of a number of sectors, considered as independent links with random change of link states in time. The proposed routing scheme was investigated with the use of a detailed simulation model, implemented in terms of a class of extended Petri nets. In simulation the following performance metrics were investigated versus time-to-live value: response ratio, relative network traffic and relative energy consumption. These metrics were considered for a number of combinations of parameters, such as the number of sinks, sensor availability and link availability. The results of simulation were compared with published characteristics of a similar model, in which sensors do not maintain any routing state information, and is proved to outperform it.     

Grid emulation for managing random sensor networks

A common assumption in sensor networks is that sensors are located according to a uniform random distribution. In this paper, we show that uniform random points on the two dimensional unit square are almost a “grid”. In particular, for a synchronous geographic sensor network we show how to emulate any grid protocol on random sensor networks, with high probability.This suggests the following framework. In order to solve a problem on a random sensor network, we solve the same problem on a grid. Then we use our emulation to make the obtained solution suitable for random sensor network. We analyze the cost of the emulation in terms of consumed energy and time. Finally, we provide some examples that illustrate our method.     

Coverage by randomly deployed wireless sensor networks

One of the main applications of wireless sensor networks is to provide proper coverage of their deployment regions. A wireless sensor network k-covers its deployment region if every point in its deployment region is within the coverage ranges of at least k sensors. In this paper, we assume that the sensors are deployed as either a Poisson point process or a uniform point process in a square or disk region, and study how the probability of the k-coverage changes with the sensing radius or the number of sensors. Our results take the complicated boundary effect into account, rather than avoiding it by assuming the toroidal metric as done in the literature.     

Chain-based barrier coverage in WSNs: toward identifying and repairing weak zones

Barrier coverage constructs a sensing barrier for detecting intruders crossing a belt region. Recent studies mostly focus on efficient algorithms to guarantee barrier coverage, with little consideration on the collaboration between individual nodes. Observing that in many situations, sensors naturally fall into several clusters (or components), for example when the sensors are deployed uniformly at random with a relatively low density, or when random sensors go down as a result of energy exhaustion, we propose to use chain as a basic scheduling unit for sensing and communication. A chain is a set of sensors whose sensing areas overlap with each other, and it can be extracted from a cluster. We present a distributed algorithm, named BARRIER, to provide barrier coverage with a low communication overhead for the wireless sensor networks (WSNs). The algorithm is able to detect weak zones that are often found in an initial deployment of a WSN, and repair them by adding an appropriate number of sensors. Theoretic analysis and simulations show that, compared with a representative previous algorithm, BARRIER significantly reduces the communication overhead and reparation cost in terms of number of sensors.     

无线传感器网络基于参数可调增强型覆盖控制算法

覆盖问题是无线传感器网络领域的一个基本问题,也是无线传感器网络特性当中的一个重点问题.如何通过某种算法达到以最少传感器节点对监测区域的有效覆盖已成为目前研究的一项重要课题.因此,提出一种增强型覆盖控制算法(Enhanced Coverage Control Algorithm, ECCA).该算法通过概率理论知识可以有效地求解出对监测区域进行有效覆盖下的最少节点,给出了传感器节点概率的期望值计算方法以及目标节点首次被传感器节点覆盖和多次覆盖后的期望值求解过程,验证随机变量相互之间不独立时的比例函数关系.仿真结果表明,ECCA算法可以使用较少的传感器节点数量完成对监测区域的有效覆盖,提高了对监测区域的覆盖质量.     

视野有限的传感器网络中分布式多目标跟踪

在传感器网络的多目标跟踪研究中,大多数现有的跟踪算法通常设定网络中所有节点具有相同的视野,即所有节点都能够得到目标的测量,但在实际中,节点的感测范围通常是有限的。针对这一问题,本文提出了一种能够在感测范围有限的多传感器网络中实现多目标跟踪的分布式概率假设密度滤波算法,该算法通过融合传感器网络视野范围内的后验概率假设密度粒子集来克服传感器节点感测范围的局限。仿真结果表明,提出的算法可以在感测范围有限的情况下实现多目标状态和数目的有效跟踪,同时可以在一定程度上抑制杂波,具有较好的跟踪稳定性。      

A Hybrid Key Predistribution Scheme for Sensor Networks Employing Spatial Retreats to Cope with Jamming Attacks

In order to provide security services in wireless sensor networks, a well-known task is to provide cryptographic keys to sensor nodes prior to deployment. It is difficult to assign secret keys for all pairs of sensor node when the number of nodes is large due to the large numbers of keys required and limited memory resources of sensor nodes. One possible solution is to randomly assign a few keys to sensor nodes and have nodes be able to connect to each other with some probability. This scheme has limitations in terms of the tradeoffs between connectivity and memory requirements. Recently, sensor deployment knowledge has been used to improve the level of connectivity while using lesser amounts of memory space. However, deployment based key predistribution schemes may cause a large number of nodes to be cryptographically isolated if nodes move after key pre-distribution. Mobility may be necessitated for reasons depending on applications or scenarios. In this paper, we consider mobility due to spatial retreat of nodes under jamming attacks as an example. Jamming attacks are easy and efficient means for disruption of the connectivity of sensors and thus the operation of a sensor network. One solution for mobile sensor nodes to overcome the impact of jamming is to perform spatial retreats by moving nodes away from jammed regions. Moved nodes may not be able to reconnect to the network because they do not have any shared secret with new neighbors at new locations if strict deployment knowledge based key predistribution is employed. In this paper, we propose a hybrid key predistribution scheme that supports spatial retreat strategies to cope with jamming attacks. Our scheme combines the properties of random and deployment knowledge based key predistribution schemes. In the presence of jamming attacks, our scheme provides high key connectivity (similar to deployment knowledge based schemes) while reducing the number of isolated nodes. We evaluate the performance of our scheme through simulations and analysis.     

Dynamic node activation in networks of rechargeable sensors

We consider a network of rechargeable sensors, deployed redundantly in a random sensing environment, and address the problem of how sensor nodes should be activated dynamically so as to maximize a generalized system performance objective. The optimal sensor activation problem is a very difficult decision question, and under Markovian assumptions on the sensor discharge/recharge periods, it represents a complex semi-Markov decision problem. With the goal of developing a practical, distributed but efficient solution to this complex, global optimization problem, we first consider the activation question for a set of sensor nodes whose coverage areas overlap completely. For this scenario, we show analytically that there exists a simple threshold activation policy that achieves a performance of at least 3/4 of the optimum over all possible policies. We extend this threshold policy to a general network setting where the coverage areas of different sensors could have partial or no overlap with each other, and show by simulations that the performance of our policy is very close to that of the globally optimal policy. Our policy is fully distributed, and requires the sensor nodes to only keep track of the node activation states in its immediate neighborhood. We also consider the effects of spatial correlation on the performance of the threshold activation policy, and the choice of the optimal threshold.     

SoC Issues for RF Smart Dust

Wireless sensor nodes are autonomous devices incorporating sensing, power, computation, and communication into one system. Applications for large scale networks of these nodes are presented in the context of their impact on the hardware design. The demand for low unit cost and multiyear lifetimes, combined with progress in CMOS and MEMS processing, are driving development of SoC solutions for sensor nodes at the cubic centimeter scale with a minimum number of off-chip components. Here, the feasibility of a complete, cubic millimeter scale, single-chip sensor node is explored by examining practical limits on process integration and energetic cost of short-range RF communication. Autonomous cubic millimeter nodes appear within reach, but process complexity and substantial sacrifices in performance involved with a true single-chip solution establish a tradeoff between integration and assembly.     

A distributed lightweight Redundancy aware Topology Control Protocol for wireless sensor networks

WSN consists of a large number of sensor nodes randomly deployed, and, in many cases, it is impossible to replace sensors when a node failure occurs. Thus, applications tend to deploy more nodes than necessary to cope with possible node failures and to increase the network lifetime, which leads to create some sensing and communication redundancy. However, sensors in the same region, may collect and forward the same information, which will waste more energy. In this paper, we propose a distributed Lightweight Redundancy aware Topology Control Protocol (LRTCP) for wireless sensor networks. It exploits the sensor redundancy in the same region by dividing the network into groups so that a connected backbone can be maintained by keeping a minimum of working nodes and turning off the redundant ones. LRTCP identifies equivalent nodes in terms of communication based on their redundancy degrees with respect of some eligibility rules. Simulation results indicate that, compared with existing distributed topology control algorithms, LRTCP improves network capacity and energy efficiency.