Processing In Wireless Sensor Networks Research Articles

A Hybrid Algorithm for Reliable and Energy-efficient Data Gathering in Wireless Sensor Networks

Reliability and energy efficiency are two important requirements of the data gathering process in wireless sensor networks. Accordingly, we propose a novel data gathering algorithm which meets these requirements. The proposed scheme categorizes the sensed data into valuable and normal data and handles each type of data based on its demands. The main requirement of valuable data is reliability. Thus, the adopted strategy to gather this type of data is to send several copies of data packets toward the sink. The rise of energy exhaustion in this scheme is tolerable. This is due to that, the valuable data is generated at a low rate. On the other hand, our main concern in gathering normal data is energy efficiency. As most of the sensed data is normal, an energy-efficient approach to gather normal data results in considerable energy conserving. Thus, we exploit clustering technique for normal data gathering. We also propose a lightweight intrusion detection system to detect malicious nodes. Simulation results and theoretical analysis confirm that our proposed algorithm provides reliability and energy efficiency at an acceptable level.

Adaptive Medium Access Control for Distributed Processing in Wireless Sensor Networks

Signal and information processing tasks over Wireless Sensor Networks can be successfully accomplished by means of a distributed implementation among the nodes. Existing distributed schemes are commonly based on iterative strategies that imply a huge demand of one-hop transmissions, which must be efficiently processed by the lower layers of the nodes. At the link layer, general purpose medium access (MAC) policies for wireless communications usually focus on avoiding collisions. These existing approaches result in a reduction of the number of simultaneous transmissions, and an underutilization of the channel as a consequence. This leads to a decrease in the performance of the distributed tasks, since an efficient channel occupation is not generally accomplished. In this paper, we propose a new MAC protocol that, besides focusing on the reliability of the communications, provides an efficient channel occupation. While the former has a direct impact on the energy consumption of usually battery powered devices, the latter affects the performance of the distributed task executed. We include both aspects in a global utility function that the nodes, relying just on local available information, aim to increase at every communication step. Furthermore, our proposal combines in a unique framework both unicast and broadcast scenarios. Through several simulation results, we show that our adaptive protocol outperforms the related literature.

Secure and Efficient Cluster-Based Range Query Processing in Wireless Sensor Networks

In wireless sensor networks, preserving privacy is more important and has attracted more attentions. Protecting data and sensor privacy while collecting and computing query results is a challenge. In cluster-based sensor networks, when a user queries a sensitive data, the adversaries can monitor original node or gain the data in cluster node. To deal with this problem, we propose a secure and efficient scheme for cluster-based query processing in wireless sensor networks. To preserve location privacy of sensors, we use anonymity method to confuse adversaries. To protect the sensitive data, we use prefix membership verification method to prevent adversaries from gaining sensitive messages collected by sensor nodes. And we analyze the security and communication cost. The results show that our scheme can efficiently protect privacy in query processing.

Cooperative Computing System for Heavy-Computation and Low-Latency Processing in Wireless Sensor Networks.

Over the past decades, hardware and software technologies for wireless sensor networks (WSNs) have significantly progressed, and WSNs are widely used in various areas including Internet of Things (IoT). In general, existing WSNs are mainly used for applications that require delay-tolerance and low-computation due to the poor resources of traditional sensor nodes in WSNs. However, compared to the traditional sensor nodes, today’s devices for WSNs have more powerful resource. Thus, sensor nodes these days not only conduct sensing and transmitting data to servers but also are able to process many operations, so more diverse applications can be applied to WSNs. Especially, many applications using audio data have been proposed because audio is one of the most widely used data types, and many mobile devices already have a built-in microphone. However, many of the applications have a requirement that heavy-operations should be done by a tight deadline, so it is difficult for a single node in WSNs to run relatively heavy applications by itself. In this paper, to overcome this limitation of WSNs, we propose a new emerging system, HeaLow, a cooperative computing system for heavy-computation and low-latency processing in WSNs. We designed HeaLow and carried out the practical implementation on real devices. We confirmed the effectiveness of HeaLow through various experiments using the real devices and simulations. Using HeaLow, nodes in WSNs are able to perform heavy-computation processes while satisfying a completion time requirement.

Broker-less middleware for WSAN performance evaluation

Over the past few years, The cost of electronic devices and sensor networks decreased rapidly enforcing almost all users’ requirements to use those devices. These devices provide low cost processing in wireless sensor networks (WSNs), As well as in wireless sensor and actuator networks (WSANs). The task of data management in WSNs is a vital issue that can be performed with limited resources such as processing, Memory and energy. Data distribution service (DDS) is a prominent standard used in the industry and academia communities to support real-time distributed systems depending on publish/subscribe (pub/sub) model. tinyDDS is a lightweight middleware that is a partial porting of the DDS and implemented over tinyOS code. The original version of tinyDDS is called the default tinyDDS (defTDDS). broker-less tinyDDS (BLTDDS) and hybrid tinyDDS (hyTDDS) are protocols that added several improvements to the defTDDS. The energy aware tinyDDS (EATDDS) protocol is proposed to deal directly with the energy consumption metric. In this paper, We conduct a comparative study between defTDDS, BLTDDS and hyTDDS in terms of throughput, PDR, End-to-end delay and energy consumption. Moreover, We propose a new protocol named an enhanced energy aware tinyDDS (E-EATDDS) that improves the energy consumption of the EATDDS protocol. We use tinyOS simulator in our implementation and evaluation. The results show that E-EATDDS outperforms BLTDDS, hyTDDS and EATDDS in terms of number of packets sent per joule.

Hierarchy of Clusters With an Idea of Minimizing the Total Energy Spent in the System

The cluster head will be chosen by the sensor nodes in the individual cluster or be pre-assigned by the user. The main advantages of clustering are the transmission of aggregated data to the base station, offers scalability for huge number of nodes and trims down energy consumption. Fundamentally, clustering could be classified into centralized clustering, distributed clustering and hybrid clustering. In centralized clustering, the cluster head is fixed. The rest of the nodes in the cluster act as member nodes. In distributed clustering, the cluster head is not fixed. The cluster head keeps on shifting form node to node within the cluster on the basis of some parameters. Hybrid clustering is the combination of both centralized clustering and distributed clustering mechanisms. This paper gives a brief overview on clustering process in wireless sensor networks. A research on the well evaluated distributed clustering algorithm Low Energy Adaptive Clustering Hierarchy (LEACH) and its followers are portrayed artistically. To overcome the drawbacks of these existing algorithms a hybrid distributed clustering model has been proposed for attaining energy efficiency to a larger scale.

Gravitational Clustering: A simple, robust and adaptive approach for distributed networks

Distributed signal processing for wireless sensor networks enables that different devices cooperate to solve different signal processing tasks. A crucial first step is to answer the question: who observes what? Recently, several distributed algorithms have been proposed, which frame the signal/object labelling problem in terms of cluster analysis after extracting source-specific features, however, the number of clusters is assumed to be known. We propose a new method called gravitational clustering to adaptively estimate the time-varying number of clusters based on a set of feature vectors. The key idea is to exploit the physical principle of gravitational force between mass units: streaming-in feature vectors are considered as mass units of fixed position in the feature space, around which mobile mass units are injected at each time instant. The cluster enumeration takes advantage of the fact that the highest attraction on the mobile mass units is exerted by regions with a high density of feature vectors, i.e., gravitational clusters. By sharing estimates among neighboring nodes via a diffusion-adaptation scheme, cooperative and distributed cluster enumeration is achieved. Numerical experiments concerning robustness against outliers, convergence and computational complexity are conducted. The application to distributed cooperative multi-view camera networks illustrates the applicability to real-world problems.

In-network data processing in wireless sensor networks using compressed sensing

One of the major energy consuming tasks in wireless sensor networks (WSNs) is the task of data transmission. The lifetime of such a network can be significantly enhanced by, minimising the in-network transmissions and dividing the transmission load symmetrically over the network. To overcome the issues of non-uniform energy dissipation and network's lifespan, a novel hierarchical compressive sampling (HCS) scheme is proposed. On the basis of the well-known hybrid compressed sensing (CS) scheme, the proposed HCS aims at minimising the overall in-network communication during the data gathering process by obtaining correlated sensor readings through a hierarchical clustering scheme. The proposed HCS is able to identify the optimal position for the application of CS in the routing structure, to achieve symmetric load distribution in a randomly deployed network. The equal distribution of transmission load is validated through a heat map generated for showing the receiving and transmission activity at each node. An energy consumption model, based on the energy required by the radio, the processor and in the CS process, is proposed and the lifetime of the network is simulated for different sink positions. Simulations prove the efficacy of the proposed HCS over various CS-based data processing schemes.

CS‐PLM: Compressive Sensing Data Gathering Algorithm Based on Packet Loss Matching in Sensor Networks

The data transmission process in Wireless Sensor Networks (WSNs) often experiences errors and packet losses due to the environmental interference. In order to address this problem, we propose a Compressive Sensing data gathering algorithm based on Packet Loss Matching (CS‐PLM). It is proven that, under tree routing, the packet loss on communication links would severely undermine the data reconstruction accuracy in Compressive Sensing (CS) based data gathering process. It is further pointed out that the packet loss in CS based data gathering exhibits the correlation effect. Meanwhile, we design a sparse observation matrix based on packet loss matching and verify that the designed matrix satisfies the Restricted Isometry Property (RIP) with a probability arbitrarily close to 1. Therefore, reliable transmission of the compressed data can be guaranteed by adopting the multipath backup routing among CS nodes. It is shown in the simulation results that, with a 60% packet loss ratio of the link, the CS‐PLM algorithm can still ensure the effective reconstruction of the data gathered by the CS algorithm and the relative reconstruction error is lower than 5%. Therefore, it is verified that the proposed algorithm could effectively alleviate the sensitivity to packet losses for the CS based data gathering algorithm on unreliable links.

Distributed Joint Optimization Routing Algorithm Based on the Analytic Hierarchy Process for Wireless Sensor Networks

As a crucial issue for routing algorithms in wireless sensor networks (WSNs), balancing energy consumption is a vital strategy to extend the network lifetime. Considering the node energy property or the node distance property, some conventional WSNs’ routing algorithms unevenly distribute traffic over the nodes, which are not optimal to maximize the network lifetime. In this letter, a distributed joint optimization routing algorithm based on the analytic hierarchy process (DORAHP) is proposed to extend WSNs lifetime. The analytic hierarchy process is applied at the transmitter side to select the next hop node by defining three key decision criteria including the residual energy of the node, the distance from the node to the base station, and the degree of the node, which aims to extend WSNs lifetime through balancing energy consumption. At length, extensive simulation experiments for DORAHP are performed and compared with the LEACH, HEED, EERC, and GASONeC algorithms. The results indicate that DORAHP has superior performance on balancing energy consumption and on extending WSNs lifetime.

Towards scalable and load-balanced mobile agents-based data aggregation for wireless sensor networks

In recent years, mobile agents have been employed for collaborative processing in wireless sensor networks (WSNs), where agents migrate from node to node, progressively aggregate data and return to the sink with the results. However, when an agent migrates through its itinerary, its size linearly grows and it may become bloated in size. We show through mathematical analysis that the existence of the bloated state problem in mobile agents-based data aggregation causes increase in node's energy consumption as well as response time. To solve the bloated state problem, we propose a scalable and load-balanced scheme for mobile agents-based data aggregation. The proposed protocol is evaluated through simulation experiments under different network scenario and compared with some well-known existing schemes. The proposed scheme shows improved performance in terms of energy consumption, response time and network lifetime.

Adaptive Neuro-Fuzzy Location Indicator in Wireless Sensor Networks

Indoor localization is a basic process in Wireless Sensor Networks (WSN) monitoring. This paper presents a new approach for localization of mobile nodes in WSNs. The proposed approach is based on the design of an adaptive fuzzy localization system. First proposed contribution is to consider the rooms of the target environment as a fuzzy sets made by adjacent zones described by a Fuzzy Location Indicator (FLI). FLI provides a fuzzy linearization of the building map hence the creation of a fuzzy linguistic model of the system. Fingerprints of the Radio Signal Strength Indicators (RSSI) are collected from different anchors according to each FLI. A Sugeno type-0 fuzzy inference system is proposed and submitted to a supervised learning through the neuro-fuzzy ANFIS algorithm. Simulation results as well as experimentations in Cynapsys company premises have proved that a good learning process leads to high success rate. Finally, a comparative study with two fuzzy localization systems proved the lower localization error average of the proposed approach.

Using Hybrid Approaches for Data Aggregation and Clustering in WSNS

The cluster head will be chosen by the sensor nodes in the individual cluster or be pre-assigned by the user. The main advantages of clustering are the transmission of aggregated data to the base station, offers scalability for huge number of nodes and trims down energy consumption. Fundamentally, clustering could be classified into centralized clustering, distributed clustering and hybrid clustering. In centralized clustering, the cluster head is fixed. The rest of the nodes in the cluster act as member nodes. In distributed clustering, the cluster head is not fixed. The cluster head keeps on shifting form node to node within the cluster on the basis of some parameters. Hybrid clustering is the combination of both centralized clustering and distributed clustering mechanisms. This paper gives a brief overview on clustering process in wireless sensor networks. A research on the well evaluated distributed clustering algorithm Low Energy Adaptive Clustering Hierarchy (LEACH) and its followers are portrayed artistically. To overcome the drawbacks of these existing algorithms a hybrid distributed clustering model has been proposed for attaining energy efficiency to a larger scale.

Efficient iceberg join processing in wireless sensor networks

A new class of monitoring applications is emerging, in which multiple embedded devices are deployed to sense the physical world and a large amount of data is injected into the network. Yet, existing monitoring algorithms usually output a result set that is trivial for users and too expensive for the resource-constraint network. In this paper, we study the problem of iceberg join processing in wireless sensor networks. The iceberg join query only includes a small fraction of data in its result set, yet, still contains the most 'interesting' and useful data relationships and linkages of the sensing data. The proposed algorithm SRJA is output sensitive and adopts a progressive refinement strategy for the query processing. Our algorithm first constructs flexible synopses according to the characteristics of the joining data, and then progressively refines these synopses to identify tuples that can match and meet the iceberg threshold in the joining regions. It fully utilises the iceberg threshold to filter out tuples that do not contribute to the final result set at early stages, saving lots of transmissions. Extensive experiments indicate that our algorithm gains a reduction up to 25% of message transmissions compared with other schemes.

Efficient iceberg join processing in wireless sensor networks

Efficient iceberg join processing in wireless sensor networks

A 915 MHz 175 $\mu \text{W}$ Receiver Using Transmitted-Reference and Shifted Limiters for 50 dB In-Band Interference Tolerance

Improving interference robustness in ultralow power (ULP) receivers (RXs) is a big challenge due to their low power budget. This paper presents an envelope detector based 915 MHz 10 kb/s ULP RX, which is fabricated in 65 nm CMOS process for wireless sensor networks and Internet of Things. Two power-efficient techniques, transmitted-reference and shifted limiter, are proposed to improve the interference robustness. The RX sensitivity is between −61 and −76 dBm. The maximum in-band signal-to-interference ratio at ±1 MHz offset is up to −50 dB while just consuming 175 $\mu \text{W}$ power from a 1 V supply.

Conceptual Agent-Based Model for Optimal Power Utilization in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are densely and largely deployed in a variety of environments to sense real-world events. Many approaches have been proposed for collecting sensory data in any wireless sensor networks in a flexible, reliable, and efficient manner. In this study, we design an efficient mobile agent-based model for data gathering in wireless sensor networks to achieve energy and timely collection of sensory information in the target scene. The current focus in wireless sensor networks is placed on energy optimization during data gathering, processing and transmission. Mobile Agents (MAs) as a piece of a program have attracted growing research interest that travels the network from node to node to compute the local data to get useful information globally. Mobile agent’s features such as autonomy, social ability, learning, and more significantly, mobility makes it a chosen technology for information processing in wireless sensor networks and other resource constrained computing environments. The nodes of the sensors are modelled and represented by the states of a Markov chain. The nodes are further grouped into clusters to save the energy of the nodes farther away from the base station. Keywords: Mobile Agents, Sensor Nodes, Wireless Sensor Networks, Clustering. DOI: https://doi.org/10.18267/j.aip.84

Adaptive Particle Filter for Nonparametric Estimation with Measurement Uncertainty in Wireless Sensor Networks.

Particle filters (PFs) are widely used for nonlinear signal processing in wireless sensor networks (WSNs). However, the measurement uncertainty makes the WSN observations unreliable to the actual case and also degrades the estimation accuracy of the PFs. In addition to the algorithm design, few works focus on improving the likelihood calculation method, since it can be pre-assumed by a given distribution model. In this paper, we propose a novel PF method, which is based on a new likelihood fusion method for WSNs and can further improve the estimation performance. We firstly use a dynamic Gaussian model to describe the nonparametric features of the measurement uncertainty. Then, we propose a likelihood adaptation method that employs the prior information and a belief factor to reduce the measurement noise. The optimal belief factor is attained by deriving the minimum Kullback–Leibler divergence. The likelihood adaptation method can be integrated into any PFs, and we use our method to develop three versions of adaptive PFs for a target tracking system using wireless sensor network. The simulation and experimental results demonstrate that our likelihood adaptation method has greatly improved the estimation performance of PFs in a high noise environment. In addition, the adaptive PFs are highly adaptable to the environment without imposing computational complexity.

Efficient Selection Scheme for Data Processing in Wireless Sensor Networks

Recently, many data processing applications in wireless sensor networks (WSNs) works efficiently by using a coverage percentage of a target sensing area and a satisfaction percentage of collected data. Therefore, the whole coverage and complete satisfaction are not needed. As a result, finding new data processing techniques that can successfully minimize the data traffic and energy consumption for maximizing the network lifetime are required. In addition, using clustering with data processing techniques is an effective topology control approach in wireless sensor networks, which can increase network scalability and lifetime. In this paper, a ( -cov,  -sat) data processing problem is introduced and a new mobile agent clustering data processing methods are proposed. The proposed methods use a clustering with a mobile agent to cover  percentage of the target area such that the satisfaction percentage of collected data is  percentage. Simulation results show that the proposed methods achieve higher improvements in network lifetime, load balance and energy consumption than the existing methods.

Rule-based inference and decomposition for distributed in-network processing in wireless sensor networks

Wireless sensor networks are application specific and necessitate the development of specific network and information processing architectures that can meet the requirements of the applications involved. A common type of application for wireless sensor networks is the event-driven reactive application, which requires reactive actions to be taken in response to events. In such applications, the interest is in the higher-level information described by complex event patterns, not in the raw sensory data of individual nodes. Although the central processing of information produces the most accurate results, it is not an energy-efficient method because it requires a continuous flow of raw sensor readings over the network. As communication operations are the most expensive in terms of energy usage, the distributed processing of information is indispensable for viable deployments of applications in wireless sensor networks. This method not only helps in reducing the total amount of packets transmitted in the network and the total energy consumed by the sensor nodes, but also produces scalable and fault-tolerant networks. For this purpose, we present two schemes that distribute information processing to appropriate nodes in the network. These schemes use reactive rules, which express relations between event patterns and actions, in order to capture reactive behavior. We also share the results of the performance of our algorithms and the simulations based on our approach that show the success of our methods in decreasing network traffic while still realizing the desired functionality.