Distributed compression in a dense microsensor network

Distributed nature of the sensor network architecture introduces unique challenges and opportunities for collaborative networked signal processing techniques that can potentially lead to significant performance gains. Many evolving low-power sensor network scenarios need to have high spatial density to enable reliable operation in the face of component node failures as well as to facilitate high spatial localization of events of interest. This induces a high level of network data redundancy, where spatially proximal sensor readings are highly correlated. We propose a new way of removing this redundancy in a completely distributed manner, i.e., without the sensors needing to talk, to one another. Our constructive framework for this problem is dubbed DISCUS (distributed source coding using syndromes) and is inspired by fundamental concepts from information theory. We review the main ideas, provide illustrations, and give the intuition behind the theory that enables this framework.We present a new domain of collaborative information communication and processing through the framework on distributed source coding. This framework enables highly effective and efficient compression across a sensor network without the need to establish inter-node communication, using well-studied and fast error-correcting coding algorithms     

Connected sensor cover: self-organization of sensor networks for efficient query execution

Spatial query execution is an essential functionality of a sensor network, where a query gathers sensor data within a specific geographic region. Redundancy within a sensor network can be exploited to reduce the communication cost incurred in execution of such queries. Any reduction in communication cost would result in an efficient use of the battery energy, which is very limited in sensors. One approach to reduce the communication cost of a query is to self-organize the network, in response to a query, into a topology that involves only a small subset of the sensors sufficient to process the query. The query is then executed using only the sensors in the constructed topology. The self-organization technique is beneficial for queries that run sufficiently long to amortize the communication cost incurred in self-organization. In this paper, we design and analyze algorithms for suchself-organization of a sensor network to reduce energy consumption. In particular, we develop the notion of a connected sensor cover and design a centralized approximation algorithm that constructs a topology involving a near-optimal connected sensor cover. We prove that the size of the constructed topology is within an O(logn) factor of the optimal size, where n is the network size. We develop a distributed self-organization version of the approximation algorithm, and propose several optimizations to reduce the communication overhead of the algorithm. We also design another distributed algorithm based on node priorities that has a further lower communication overhead, but does not provide any guarantee on the size of the connected sensor cover constructed. Finally, we evaluate the distributed algorithms using simulations and show that our approaches results in significant communication cost reductions.     

A distributed self-deployment algorithm for the coverage of mobile wireless sensor networks

This letter addresses a coverage problem through the use of a self-deployed mobile wireless sensor network. We propose a distributed motion coordination algorithm for the mobile sensors to autonomously form a sensor barrier between two given landmarks to achieve the barrier coverage. The algorithm is developed based on some simple rules that are computationally efficient and require less communication overhead.     

A communication‐efficient framework for outlier‐free data reporting in data‐gathering sensor networks

In this paper, we address the problem of reducing the communication cost and hence the energy costs incurred in data‐gathering applications of a sensor network. Environmental data depicts a huge amount of correlation in both the spatial and temporal domains. We exploit these temporal–spatial correlations to address the aforementioned problem. More specifically, we propose a framework that partitions the physical sensor network topology into a number of feature regions. Each sensor node builds a data model that represents the underlying structure of the data. A representative node in each feature region communicates only the model coefficients to the sink, which then uses them to answer queries. The temporal and spatial similarity has special meaning in outlier cleaning too. We use a modified z‐score technique to precisely label the outliers and use the spatial similarity to confirm whether the outliers are due to a true change in the phenomenon under study or due to faulty sensor nodes. Copyright © 2008 John Wiley & Sons, Ltd.     

Temporal Event Ordering with Fault Tolerance for Wireless Sensor and Actuator Networks

Temporal event ordering is an important issue in wireless sensor and actuator networks (WSAN) since actuators perform correct actions based on correct ordering among the time-related events. For temporal event ordering algorithms, they ensure that events captured from different sensors should be in the order. However, most of temporal ordering algorithms focus on the delay to transmit packets without considering the delay due to data loss. In the latter condition, the temporal ordering among events may be incorrect since some sensed data are lost due to the fail communication link or sensor. However, there is no literature for addressing the fault tolerance under the temporal event ordering of WSAN. Therefore, we propose a temporal event ordering with fault tolerance (TVOFT) in this paper. Differed from other temporal event ordering algorithms, TVOFT uses the routing table of each node with a acknowledge message once time to ensure the correct ordering of the time-related events. Therefore, the overhead of control packets also could be reduced in TVOFT. The simulations results demonstrate that TVOFT has the better correct event ordering rate and the less overhead of control packets than other temporal ordering algorithms even if the communication link or the sensor is fail.     

Location-centric storage and query in wireless sensor networks

Location-centric storage (LCS) is envisioned as a promising scheme for robust and user-friendly on-demand data storage in networking environments such as the roadway sensor networks [Xing K et al. J Parallel Distributed Comput 67:336–345, 2007; Xing K et al. in: IEEE wireless communication and networking conference (WCNC), 2005]. In this paper, we analyze the performance of LCS in terms of storage and query overheads. This study indicates that LCS utilizes network resource efficiently and achieves good scalability. In particular, the storage overhead of sensors is independent of the network size, and is evenly distributed across the network. We also propose two algorithms for data retrieval in LCS-enabled one-dimensional and two-dimensional sensor networks. Our algorithms guarantee that acquiring the stored data of any event only takes a small number of communication hops to query a small number of sensors.     

SCT Based Adaptive Data Aggregation for Wireless Sensor Networks

Communication overhead is a major concern in wireless sensor networks because of inherent behavior of resource constrained sensors. To degrade the communication overhead, a technique called data aggregation is employed. The data aggregation results are used to make crucial decisions. Certain applications apply approximate data aggregation in order to reduce communication overhead and energy levels. Specifically, we propose a technique called semantic correlation tree, which divides a sensor network into ring-like structure. Each ring in sensor network is divided into sectors, and each sector consists of collection of sensor nodes. For each sector, there will be a sector head that is aggregator node, the aggregation will be performed at sector head and determines data association on each sector head to approximate data on sink node. We propose a doorway algorithm to approximate the sensor node readings in sector head instead of sending all sensed data. The main idea of doorway algorithm is to reduce the congestion and also the communication cost among sensor nodes and sector head. This novel approach will avoid congestion by controlling the size of the queue and marking packets. Specifically, we propose a local estimation model to generate a new sensor reading from historic data. The sensor node sends each one of its parameter to sector head, instead of raw data. The doorway algorithm is utilized to approximate data with minimum and maximum bound value. This novel approach, aggregate the data approximately and efficiently with limited energy. The results demonstrate accuracy and efficiency improvement in data aggregation.     

Spatio-temporal sampling rates and energy efficiency in wireless sensor networks

A multi-hop network of wireless sensors can be used to gather spatio-temporal samples of a physical phenomenon and transmit these samples to a processing center. This paper addresses an important issue in the design of such networks: determining the spatio-temporal sampling rate of the network under conditions of minimum energy usage. A new collision-free protocol for gathering sensor data is used to obtain analytical results that characterize the tradeoffs among sensor density, energy usage, throughput, delay, temporal sampling rates and spatial sampling rates in wireless sensor networks. We also show that the lower bound on the delay incurred in gathering data is O(k/sup 2/n) in a clustered network of n sensors with at most k hops between any sensor and its clusterhead (CH). Simulation results on the tradeoff between the achievable spatial sampling rates and the achievable temporal sampling rates when IEEE 802.11 distributed coordination function (DCF) is used as the media access scheme are provided and compared with the analytical results obtained in this paper.     

Robust location detection with sensor networks

We propose a novel framework for location detection with sensor networks, based on the theory of identifying codes. The key idea of this approach is to allow sensor coverage areas to overlap so that each resolvable position is covered by a unique set of sensors. In this setting, determining a sensor-placement with a minimum number of sensors is equivalent to constructing an optimal identifying code, an NP-complete problem in general. We, thus, propose and analyze new polynomial-time algorithms for generating irreducible (but not necessarily optimal) codes for arbitrary topologies. Our algorithms incorporate robustness properties that are critically needed in harsh environments. We further introduce distributed versions of these algorithms, allowing sensors to self-organize and determine a (robust) identifying code without any central coordination. Through analysis and simulation, we show that our algorithms produce nearly optimal solutions for a wide range of parameters. In addition, we demonstrate a tradeoff between system robustness and the number of active sensors (which is related to the expected lifetime of the system). Finally, we present experimental results, obtained on a small testbed, that demonstrate the feasibility of our approach.     

Deploying Wireless Sensor Networks under Limited Mobility Constraints

In this paper, we study the issue of sensor network deployment using limited mobility sensors. By limited mobility, we mean that the maximum distance that sensors are capable of moving to is limited. Given an initial deployment of limited mobility sensors in a field clustered into multiple regions, our deployment problem is to determine a movement plan for the sensors to minimize the variance in number of sensors among the regions and simultaneously minimize the sensor movements. Our methodology to solve this problem is to transfer the nonlinear variance/movement minimization problem into a linear optimization problem through appropriate weight assignments to regions. In this methodology, the regions are assigned weights corresponding to the number of sensors needed. During sensor movements across regions, larger weight regions are given higher priority compared to smaller weight regions, while simultaneously ensuring a minimum number of sensor movements. Following the above methodology, we propose a set of algorithms to our deployment problem. Our first algorithm is the optimal maximum flow-based (OMF) centralized algorithm. Here, the optimal movement plan for sensors is obtained based on determining the minimum cost maximum weighted flow to the regions in the network. We then propose the simple peak-pit-based distributed (SPP) algorithm that uses local requests and responses for sensor movements. Using extensive simulations, we demonstrate the effectiveness of our algorithms from the perspective of variance minimization, number of sensor movements, and messaging overhead under different initial deployment scenarios.     

An improved key distribution mechanism for large-scale hierarchical wireless sensor networks

Wireless sensor networks are often deployed in hostile environments and operated on an unattended mode. In order to protect the sensitive data and the sensor readings, secret keys should be used to encrypt the exchanged messages between communicating nodes. Due to their expensive energy consumption and hardware requirements, asymmetric key based cryptographies are not suitable for resource-constrained wireless sensors. Several symmetric-key pre-distribution protocols have been investigated recently to establish secure links between sensor nodes, but most of them are not scalable due to their linearly increased communication and key storage overheads. Furthermore, existing protocols cannot provide sufficient security when the number of compromised nodes exceeds a critical value. To address these limitations, we propose an improved key distribution mechanism for large-scale wireless sensor networks. Based on a hierarchical network model and bivariate polynomial-key generation mechanism, our scheme guarantees that two communicating parties can establish a unique pairwise key between them. Compared with existing protocols, our scheme can provide sufficient security no matter how many sensors are compromised. Fixed key storage overhead, full network connectivity, and low communication overhead can also be achieved by the proposed scheme.     

Distributed fault-tolerant channel allocation for cellular networks

A channel allocation algorithm includes channel acquisition and channel selection algorithms. Most of the previous work concentrates on the channel selection algorithm since early channel acquisition algorithms are centralized and rely on a mobile switching center (MSC) to accomplish channel acquisition. Distributed channel acquisition algorithms have received considerable attention due to their high reliability and scalability. However, in these algorithms, a borrower needs to consult with its interference neighbors in order to borrow a channel. Thus, the borrower fails to borrow channels when it cannot communicate with any interference neighbor. In real-life networks, under heavy traffic load, a cell has a large probability to experience an intermittent network congestion or even a communication link failure. In existing distributed algorithms, since a cell has to consult with a large number of interference neighbors to borrow a channel, the failure rate will be much higher under heavy traffic load. Therefore, previous distributed channel allocation algorithms are not suitable for real-life networks. We first propose a fault-tolerant channel acquisition algorithm which tolerates communication link failures and node (MH or MSS) failures. Then, we present a channel selection algorithm and integrate it into the distributed acquisition algorithm. Detailed simulation experiments are carried out in order to evaluate our proposed methodology. Simulation results show that our algorithm significantly reduces the failure rate under network congestion, communication link failures, and node failures compared to nonfault-tolerant channel allocation algorithms. Moreover, our algorithm has low message overhead compared to known distributed channel allocation algorithms, and outperforms them in terms of failure rate under uniform as well as nonuniform traffic distribution     

SBK: A Self-Configuring Framework for Bootstrapping Keys in Sensor Networks

Key pre-distribution has been claimed to be the only viable approach for establishing shared keys between neighboring sensors after deployment for a typical sensor network. However, none of the proposed key pre-distribution schemes simultaneously achieves good performance in terms of scalability in network size, key-sharing probability between neighboring sensors, memory overhead for keying information storage, and resilience against node capture attacks. In this paper, we propose SBK, an in-situ self-configuring framework to bootstrap keys in large-scale sensor networks. SBK is fundamentally different compared to all key pre-distribution schemes. It requires no keying information pre-deployment. In SBK, sensors differentiate their roles as either service nodes or worker nodes after deployment. Service sensors construct key spaces, and distribute keying information in order for worker sensors to bootstrap pairwise keys. An improved scheme, iSBK, is also proposed to speed up the bootstrapping procedure. We conduct both theoretical analysis and simulation study to evaluate the performances of SBK and iSBK. To the best of our knowledge, SBK and iSBK are the only key establishment protocols that simultaneously achieve good performance in scalability, key-sharing probability, storage overhead, and resilience against node capture attacks.     

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.     

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.     

Communication architecture for processing spatio-temporal continuous queries in sensor networks

Wireless sensor networks have revolutionized distributed micro-sensing because of their ease of deployment, ad hoc connectivity and cost-effectiveness. They have also enabled collecting and monitoring data from a very large area or possibly several independent areas geographically separated from each other and such a process is known as spatio-temporal data monitoring. In this paper, we define an energy-aware routing infrastructure that enables distributed query processing and supports processing of spatio-temporal queries within the network. As operator execution demands high computation capability, we propose a possible use of a heterogeneous sensor network where query operators are assigned to sparsely-deployed resource-rich nodes within a dense network of low power sensor nodes. We have designed an adaptive, decentralized, low communication overhead algorithm to determine optimal operator placement on the resource-rich nodes such that data transfer cost in the network is minimized. To the best of our knowledge, this is the first attempt to build an energy-aware communication architecture to enable in-network processing of spatio-temporal queries.     

Range-Free Mobile Sensor Localization and a Novel Obstacle Detection Technique

Mobile sensor localization is a challenging problem in wireless sensor networks. Due to mobility, it is difficult to find exact position of the sensors at any time instance. The aim of localization is to minimize positioning errors of the mobile sensors. In this paper we propose two range-free distributed localization algorithms for mobile sensors with static anchors. Both the algorithms depend on selection of beacon points. First we assume that mobile sensors move straight during localization which helps us to provide an upper bound on localization error. Certain applications may not allow sensors to move in a straight line. Obstacles may also obstruct path of sensors. Moreover beacon point selection becomes difficult in presence of obstacles. To address these issues, we propose another localization algorithm with an obstacle detection technique which selects correct beacon points for localization in presence of obstacles. Simulation results show improvements in performance over existing algorithms.     

Location Update versus Paging Trade-Off in Cellular Networks: An Approach Based on Vector Quantization

In this paper, we propose two information-theoretic techniques for efficiently trading off the location update and paging costs associated with mobility management in wireless cellular networks. Previous approaches always attempt to accurately convey a mobile's movement sequence and hence cannot reduce the signaling cost below the entropy bound. Our proposed techniques, however, exploit the rate distortion theory to arbitrarily reduce the update cost at the expense of an increase in the corresponding paging overhead. To this end, we describe two location tracking algorithms based on spatial quantization and temporal quantization, which first quantize the movement sequence into a smaller set of codewords and then report a compressed representation of the codeword sequence. Although the spatial quantization algorithm clusters individual cells into registration areas, the more powerful temporal quantization algorithm groups sets of consecutive movement patterns. The quantizers themselves are adaptive and periodically reconfigure to accommodate changes in the mobile's movement pattern. Simulation study with synthetic and real movement traces for both single-system and multisystem cellular networks demonstrate that the proposed algorithms can reduce the mobile's update frequency to 3-4 updates/day with reasonable paging cost, low computational complexity, storage overhead, and codebook updates.     

Deriving an upper bound on the average operation time of a wireless sensor network

One of the main challenges in implementing wireless sensor networks (WSN) is to minimize power consumption in order to maximize network lifetime. To accomplish this, we derive an upper bound on the average network lifetime. The upper bound was derived for a code division multiple access (CDMA) communication system. The communication procedure that consumes most of the energy is transmission. But, it is possible to reduce energy consumption by increasing the antenna gain of the base station which collects information from the sensors. However, a problem that arises is that the spot size of high gain antenna is smaller than low gain antenna and as a result the base station provides only partial coverage to the network, which is obviously an undesirable situation. Therefore we propose to shape the base-station antenna gain in such a way that maximizes network lifetime, by taking advantage of the spatial nonuniformity distribution of the sensors on the ground.     

P&;P: an asynchronous and distributed protocol for mobile sensor deployment

The use of wireless mobile sensors is of great relevance for a number of strategic applications devoted to monitoring critical areas where sensors can not be deployed manually. Mobile sensors can adapt their position on the basis of a local evaluation of coverage, thus permitting an autonomous deployment. Several algorithms have been proposed to deploy mobile sensors over an area of interest. The applicability of these approaches largely depends on a proper formalization of rigorous rules to coordinate sensor movements, solve local conflicts and manage possible failures of communications and devices. In this paper we introduce P&P, a communication protocol that permits a correct and efficient coordination of sensor movements in agreement with the Push & Pull algorithm. We deeply investigate and solve the problems that may occur when coordinating asynchronous local decisions in the presence of an unreliable transmission medium and possibly faulty devices such as in the typical working scenario of mobile sensor networks. Simulation results show the performance of our protocol under a range of operative settings, including conflict situations and irregularly shaped target areas. Furthermore, a performance comparison between the P&P protocol and one of the best solutions based on the virtual force approach, shows the superiority of our proposal in terms of deployment time, message exchanges and energy consumption.