Large Wireless Sensor Networks Research Articles

Relay Selection for Geographical Forwarding in Sleep-Wake Cycling Wireless Sensor Networks

Our work is motivated by geographical forwarding of sporadic alarm packets to a base station in a wireless sensor network (WSN), where the nodes are sleep-wake cycling periodically and asynchronously. We seek to develop local forwarding algorithms that can be tuned so as to tradeoff the end-to-end delay against a total cost, such as the hop count or total energy. Our approach is to solve, at each forwarding node enroute to the sink, the local forwarding problem of minimizing one-hop waiting delay subject to a lower bound constraint on a suitable reward offered by the next-hop relay; the constraint serves to tune the tradeoff. The reward metric used for the local problem is based on the end-to-end total cost objective (for instance, when the total cost is hop count, we choose to use the progress toward sink made by a relay as the reward). The forwarding node, to begin with, is uncertain about the number of relays, their wake-up times, and the reward values, but knows the probability distributions of these quantities. At each relay wake-up instant, when a relay reveals its reward value, the forwarding node's problem is to forward the packet or to wait for further relays to wake-up. In terms of the operations research literature, our work can be considered as a variant of the asset selling problem. We formulate our local forwarding problem as a partially observable Markov decision process (POMDP) and obtain inner and outer bounds for the optimal policy. Motivated by the computational complexity involved in the policies derived out of these bounds, we formulate an alternate simplified model, the optimal policy for which is a simple threshold rule. We provide simulation results to compare the performance of the inner and outer bound policies against the simple policy, and also against the optimal policy when the source knows the exact number of relays. Observing the good performance and the ease of implementation of the simple policy, we apply it to our motivating problem, i.e., local geographical routing of sporadic alarm packets in a large WSN. We compare the end-to-end performance (i.e., average total delay and average total cost) obtained by the simple policy, when used for local geographical forwarding, against that obtained by the globally optimal forwarding algorithm proposed by Kim et al.

Multi-Parametric Clustering for Sensor Node Coordination in Cognitive Wireless Sensor Networks

The deployment of wireless sensor networks for healthcare applications have been motivated and driven by the increasing demand for real-time monitoring of patients in hospital and large disaster response environments. A major challenge in developing such sensor networks is the need for coordinating a large number of randomly deployed sensor nodes. In this study, we propose a multi-parametric clustering scheme designed to aid in the coordination of sensor nodes within cognitive wireless sensor networks. In the proposed scheme, sensor nodes are clustered together based on similar network behaviour across multiple network parameters, such as channel availability, interference characteristics, and topological characteristics, followed by mechanisms for forming, joining and switching clusters. Extensive performance evaluation is conducted to study the impact on important factors such as clustering overhead, cluster joining estimation error, interference probability, as well as probability of reclustering. Results show that the proposed clustering scheme can be an excellent candidate for use in large scale cognitive wireless sensor network deployments with high dynamics.

A Modified clustering for LEACH algorithm in WSN

Node clustering and data aggregation are popular techniques to reduce energy consumption in large Wireless Sensor Networks (WSN). Cluster based routing is always a hot research area in wireless sensor networks. Classical LEACH protocol has many advantages in energy efficiency, data aggregation and so on. However, determining number of clusters present in a network is an important problem. Conventional clustering techniques generally assume this parameter to be user supplied. There exist very few techniques that can solve the problem of automatic detection of number of clusters satisfactorily. Some of these techniques rely on user supplied information, while others use cluster validity indices. In this paper, we proposed a rather simple method to identify the number of clusters that can give satisfactory results. Proposed method is compared with classical LEACH protocol and found to be giving better results.

Approximating Extremely Large Networks via Continuum Limits

This paper is concerned with modeling of networks with an extremely large number of components using partial differential equations (PDEs). This modeling method is based on the convergence of a sequence of underlying Markov chains of the network indexed by N, the number of components in the network. As N goes to infinity, the sequence converges to a continuum limit, which is the solution of a certain PDE. We provide sufficient conditions for the convergence and characterize the rate of convergence. As an application, we model large wireless sensor networks by PDEs. While traditional Monte Carlo simulation for extremely large networks is practically infeasible, PDEs can be solved with reasonable computation overhead using well-established mathematical tools.

Scalable Cluster-based Routing in Large Wireless Sensor Networks

Large control overhead is the leading factor limiting the scalability of wireless sensor networks (WSNs). Clustering network nodes is an efficient solution, and Passive Clustering (PC) is one of the most efficient clustering methods. In this letter, we propose an improved PC-based route building scheme, named Route Reply (RREP) Broadcast with Passive Clustering (in short RBPC). Through broadcasting RREP packets on an expanding ring to build route, sensor nodes cache their route to the sink node, which reduces the total route search times and thus the control overhead. Simulation results are also provided to show the efficiency of the proposed RBPC in terms of reduced overhead and energy consumption, and it is also shown that RBPC outperforms AODV/PC in the delivery ratio.

Cluster-based mobility adaptive MAC scheme for wireless sensor networks

Due to uncertain running conditions and dynamic fluctuation of network topology, wireless sensor networks (WSNs) always runs in an unexpected open environment. As the theme for the centerpiece, medium access control (MAC) protocol with superior performance is supposed to easily accommodate network changes. Most of the existing MAC protocols for WSNs, which usually provide very had network performance in scenarios involving mobile sensors, are aiming at stationary deployment application. For the energy-constrained attribute, handling mobility in WSNs is facing a significant challenge. In order to overcome these disadvantages, such as latency, extra energy consumption in fast connection for mobile nodes moving from one cluster to another, this paper puts forward a new adaptive mobility-aware MAC protocol named CM-MAC for large scale cluster-based WSNs. CM-MAC protocol proposes 1) a new cluster-head selection algorithm and 2) a priority-based bit-map-assisted (BMA) scheduling strategy. By applying the novel approach, CM-MAC achieves better performance by providing analytical models for CM-MAC, pure BMA [1], and low-energy adaptive clustering hierarchy (LEACH) [2]. CM-MAC performs better than these protocols in terms of energy efficiency and end to end latency in mobile scenarios.

Enhanced Correlation Estimators for Distributed Source Coding in Large Wireless Sensor Networks

In this paper, we propose two estimators based on correlation parameters for the two key steps of a practical distributed source coding (DSC) scheme, namely: 1) the computation of the side-information at the receiver side and 2) the estimation of the required number of bits to compress the readings in order to guarantee a certain symbol error probability. We show that using the proposed estimators, the DSC algorithm performs better in terms of the compression rate and the symbol error rate. In particular, this improvement is especially significant when the number of snapshots used in the training phase is only slightly larger than the observation vector. However, when the number of snapshots is much higher than the observation dimension, our proposed estimators perform similarly to the classical estimators.

Group-Ordered SPRT for Decentralized Detection

The problem of decentralized detection in a large wireless sensor network is considered. An adaptive decentralized detection scheme, group-ordered sequential probability ratio test (GO-SPRT), is proposed. This scheme groups sensors according to the informativeness of their data. Fusion center collects sensor data sequentially, starting from the most informative data and terminates the process when the target performance is reached. Wald's approximations are shown to be applicable even though the problem setting deviates from that of the traditional sequential probability ratio test (SPRT). To analyze the efficiency of GO-SPRT, the asymptotic equivalence between the average sample number of GO-SPRT, which is a function of a multinomial random variable, and a function of a normal random variable, is established. Closed-form approximations for the average sample number are then obtained. Compared with fixed sample size test and traditional SPRT, the proposed scheme achieves significant savings in the cost of data fusion.

Distributed Compressive Sampling for Lifetime Optimization in Dense Wireless Sensor Networks

The problem of data sampling and collection in wireless sensor networks (WSNs) is becoming critical as larger networks are being deployed. Increasing network size poses significant data collection challenges, for what concerns sampling and transmission coordination as well as network lifetime. To tackle these problems, in-network compression techniques without centralized coordination are becoming important solutions to extend lifetime. In this paper, we consider a scenario in which a large WSN, based on ZigBee protocol, is used for monitoring (e.g., building, industry, etc.). We propose a new algorithm for in-network compression aiming at longer network lifetime. Our approach is fully distributed: each node autonomously takes a decision about the compression and forwarding scheme to minimize the number of packets to transmit. Performance is investigated with respect to network size using datasets gathered by a real-life deployment. An enhanced version of the algorithm is also introduced to take into account the energy spent in compression. Experiments demonstrate that the approach helps finding an optimal tradeoff between the energy spent in transmission and data compression.

Distributed data collection and storage algorithms for collaborative learning vision sensor devices with applications to pilgrimage

This work presents novel distributed data collection and storage algorithms for collaborative learning Wireless Sensor Networks (WSNs). In a large WSN, consider n sensor devices distributed randomly to acquire information and learn about a certain field. Such sensors have less power, small bandwidth, and short memory, and they might disappear from the network after certain time of operations. We propose two Distributed Data Storage Algorithms (DSAs), denoted by DSA-I and DSA-II, to solve this problem. In DSA-I, where the value of n is known for each learning sensor, we show that this algorithm is efficient in terms of the encoding/decoding operations. Furthermore, each node uses network flooding to disseminate its data throughout the network using mixing time approximately O(n). In DSA-II, it is assumed that dissemination of the data does not depend on the total number of network nodes, we show that the encoding operations take O(Cμ²), where μ is the mean degree of the network graph and C is a system parameter. Performance of these two algorithms matches the derived theoretical results. Finally, these two algorithms can be used for monitoring and measuring certain phenomenon in camp tents located in the Minna field in south-east side of Makkah.

Robust Distributed Detection with Total Power Constraint in Large Wireless Sensor Networks

In practical problems of signal detection, it is quite common that the underlying noise distribution is not Gaussian and may vary in a wide range from light- to heavy-tailed forms. To design a robust fusion rule for distributed detection in wireless sensor networks, an asymptotic maximin approach is used by introducing weak signals in the canonical parallel fusion model. Explicit formulas for the detection and false alarm probabilities are derived. The analytic results are written out for the classes of nondegenerate, with a bounded variance and contaminated Gaussian noise distributions. Numerical and simulation results are obtained to justify robustness and asymptotic characteristics of the proposed fusion rule.

Design and Implementation of a Firmware Update Protocol for Resource Constrained Wireless Sensor Networks

Resource constrained Wireless Sensor Networks (WSNs) require an automated firmware updating protocol for adding new features or error fixes. Reprogramming nodes manually is often impractical or even impossible. Current update protocols require a large external memory or external WSN transport protocol. This paper presents the design, implementation, and experiments of a Program Image Dissemination Protocol (PIDP) for autonomous WSNs. It is reliable, lightweight and it supports multi-hopping. PIDP does not require external memory, is independent of the WSN implementation, transfers firmware, and reprograms the whole program image. It was implemented on a node platform with an 8-bit microcontroller and a 2.4 GHz radio. Implementation requires 22 bytes of data memory and less than 7 kilobytes of program memory. PIDP updates 178 nodes within 5 hours. One update consumes under 1‰ of the energy of two AA batteries.

Impact of Mobility Models on MMS Routing in Wireless Sensor Networks

In this paper, we describe about the performance of different mobility models on MMS Routing. MMS routing is the technique used for large wireless sensor networks where MMS indicates multiple mobile sinks. The mobility of sink is considered in random manner in the basic MMS routing. Here we show the effect of different mobility models in the sink‟s mobility. From the observations what we got from simulations we decide that wind mobility will be more energy efficient and it is good to be selected for wireless sensor network.

Linear Precoders for the Detection of a Gaussian Process in Wireless Sensors Networks

We investigate the performance of Neyman-Pearson detection of a stationary Gaussian process in noise, using a large wireless sensor network (WSN). In our model, each sensor compresses its observation sequence using a linear precoder and a final decision is taken by a fusion center (FC) based on the compressed information. Two families of precoders are studied: random i.i.d. precoders and orthogonal precoders. We analyse their performance under a regime where both the number of sensors <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> and the number of samples <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> per sensor tend to infinity at the same rate, that is, <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> / <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> → <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</i> ∈ [0,1]. Contributions are as follows. 1) Using results from random matrix theory and large Toeplitz matrices, we prove that, when the above families of precoders are used, the miss probability of the Neyman-Pearson detector converges exponentially to zero. Closed form expressions of the corresponding error exponents are derived. 2) In particular, we propose a practical orthogonal precoding strategy, the Principal Frequencies Strategy (PFS), which achieves the best error exponent among all orthogonal strategies, and which requires very little signaling overhead between the central processor and the nodes of the network. 3) When the PFS is used, a simplified low-complexity testing procedure can be implemented at the FC. We show that the proposed suboptimal test enjoys the same error exponent as the Neyman-Pearson test, which indicates a similar asymptotic behavior of the performance. We illustrate our findings by numerical experiments on several examples.

Energy Efficient Zone Division Multihop Hierarchical Clustering Algorithm for Load Balancing in Wireless Sensor Network

Wireless sensor nodes are use most embedded computing application. Multihop cluster hierarchy has been presented for large wireless sensor networks (WSNs) that can provide scalable routing, data aggregation, and querying. The energy consumption rate for sensors in a WSN varies greatly based on the protocols the sensors use for communications. In this paper we present a cluster based routing algorithm. One of our main goals is to design the energy efficient routing protocol. Here we try to solve the usual problems of WSNs. We know the efficiency of WSNs depend upon the distance between node to base station and the amount of data to be transferred and the performance of clustering is greatly influenced by the selection of cluster-heads, which are in charge of creating clusters and controlling member nodes. This algorithm makes the best use of node with low number of cluster head know as super node. Here we divided the full region in four equal zones and the centre area of the region is used to select for super node. Each zone is considered separately and the zone may be or not divided further that’s depending upon the density of nodes in that zone and capability of the super node. This algorithm forms multilayer communication. The no of layer depends on the network current load and statistics. Our algorithm is easily extended to generate a hierarchy of cluster heads to obtain better network management and energy efficiency.

A survey on clustering algorithms for wireless sensor networks

A wireless sensor network (WSN) consisting of a large number of tiny sensors can be an effective tool for gathering data in diverse kinds of environments. The data collected by each sensor is communicated to the base station, which forwards the data to the end user. Clustering is introduced to WSNs because it has proven to be an effective approach to provide better data aggregation and scalability for large WSNs. Clustering also conserves the limited energy resources of the sensors. This paper synthesises existing clustering algorithms in WSNs and highlights the challenges in clustering.

An intelligent agent-based routing structure for mobile sinks in WSNs

Mobile sink brings new challenges to densely deployed and large wireless sensor networks (WSNs). When the sink moves, frequent location updates from the sink can generate excessive power consumption of sensors. In this paper, we propose IAR, an Intelligent Agent-based Routing protocol that provides efficient data delivery to mobile sink. Proposed algorithm reduces signal overhead and improve degraded route called triangular routing problem. We have evaluated IAR performance through both mathematical analysis and simulation experiments. The results show that our scheme effectively supports sink mobility with low overhead and the improvement of triangular routing problem.

Adaptive packet prioritisation for large wireless sensor networks

Large wireless sensor networks consisting of hundreds of devices are often used to conduct distinct monitoring tasks simultaneously. Furthermore, the significant events that can be detected by the network may be detected in many different parts of the network. As a consequence, these multiple tasks and diverse events may also cause resource contention and degradation of network quality of service. For instance, congestion created by regularly occurring events which do not need urgent forwarding to the monitoring centres may create delays for other events which need to be reported very rapidly. The Random Re-Routing (RRR) protocol for wireless sensor networks has therefore been proposed to adaptively prioritise the packets of certain urgent events above those of other less urgent events which may be of a more routine nature. In this paper we extend the RRR protocol to improve performance, and we use simulation experiments to illustrate heuristic algorithms for matching routing priorities to the requirements of the sensor network's monitoring tasks. The approaches are based on selecting the protocol's parameters prior to network deployment, and on using an acknowledgement mechanism for adapting the parameters during network operation.

Structural damage detection robust against time synchronization errors

Structural damage detection based on wireless sensor networks can be affected significantlyby time synchronization errors among sensors. Precise time synchronization of sensor nodeshas been viewed as crucial for addressing this issue. However, precise time synchronizationover a long period of time is often impractical in large wireless sensor networks due to twoinherent challenges. First, time synchronization needs to be performed periodically,requiring frequent wireless communication among sensors at significant energy cost.Second, significant time synchronization errors may result from node failures which arelikely to occur during long-term deployment over civil infrastructures. In this paper, adamage detection approach is proposed that is robust against time synchronizationerrors in wireless sensor networks. The paper first examines the ways in whichtime synchronization errors distort identified mode shapes, and then proposes astrategy for reducing distortion in the identified mode shapes. Modified values forthese identified mode shapes are then used in conjunction with flexibility-baseddamage detection methods to localize damage. This alternative approach relaxesthe need for frequent sensor synchronization and can tolerate significant timesynchronization errors caused by node failures. The proposed approach is successfullydemonstrated through numerical simulations and experimental tests in a lab.

An Energy-Balanced Clustering Routing Algorithm for Wireless Sensor Network

In this paper, we consider a network of energy constrained sensors deployed over a region. Each sensor node in such a network is systematically gathering and transmitting sensed data to a base station (via cluster- heads). This paper focuses on reducing the power consumption of wireless sensor networks. Firstly, we proposed an Energy-balanced Clustering Routing Algorithm called LEACH-L, which is suitable for a large scope wireless sensor network. Secondly, optimum hop-counts are deduced. Lastly, optimum position of transmitting node is estimated. Simulation results show that our modified scheme can extend the network lifetime by up to 80% before first node dies in the network. Through both theoretical analysis and numerical simulations, it is shown that the proposed algorithm achieves higher performance than the existing clustering algorithms such as LEACH, LEACH-M.