Large Sensor Networks Research Articles

Selective-path clustered based routing protocol for large scale wireless sensor networks

Wireless sensor networks (WSNs) consist of large number of small low-powered electronic sensor devices. Therefore, reliable power saving schemes must be developed in order to manage the energy dissipated and extend the network life and functionality for a meaningful time. Arranging sensor nodes (SNs) into clusters has been viewed as an efficient approach of balancing the energy dissipated in WSNs. Regardless, a clustered WSN needs a robust energy efficient (EE) and energy balanced (EB) routing communication protocol. This paper proposes a selective-path priority table (SPT) EE and EB routing protocol (RP) ideal for most conventional clustering algorithms. The SPT RP forms the priority table by prioritising the k nearest multi-hop paths to the cluster heads (CHs) and/or base station (BS) based on some robust rules. These rules are compiled using a combination of some routing metrics which include the residual energy (RE) and transmission range/power. Firstly, the simulation results indicate that the SPT RP can be combined with most of the conventional clustering algorithm irrespective of the number of deployed SNs. Secondly, the performance of the SPT RP improves as the value of the selected number of routing paths (k) increase. More so, the simulation results show that the SPT RP reduces the energy dissipated and can prolong the network life and throughput as compared to related routing schemes.

Making Decisions by Unlabeled Bits

In a statistical decision problem, one observes independent samples and is asked to decide between two mutually exclusive states of nature. With knowledge of the data distributions under the two hypotheses, it is known that the asymptotically optimal error probabilities converge to zero at a rate given by the error exponent function, which quantifies the information for detection contained in the data. As a new element, suppose that the decision problem is to be solved without data labeling, i.e., with no knowledge of which observation comes from which distribution. What is the error exponent in this case? How much information for detection is contained in the samples and how much is lost with the labels? This problem — unlabeled detection — is of theoretical relevance per se , but is also attracting growing practical interest, because loss of labels often occurs naturally in big-data settings, user profiling in social networks; and may be deliberate, for example to save bandwidth in inference involving large sensor networks. For binary unlabeled observations and focusing on the low-detectability regime, we derive simple closed-form expressions for the error exponent and related quantities (Chernoff-Stein exponent, Chernoff information), providing new insights. Practical algorithms are then discussed, showing that many decision algorithms proposed in the literature reduce for binary data to simple forms, which highlight their properties and relative merits. A detector with close-to-optimum performance for a wide class of detection problems is proposed.

Prioritization and Alert Fusion in Distributed IoT Sensors Using Kademlia Based Distributed Hash Tables

Distributed intrusion detection systems (IDS) are primarily deployed across the network to monitor, detect, and report anomalies, as well as to respond in real-time. Predominantly, an IDS is equipped with a set of rules that it needs to infer to be able to perform efficient detection. However, reducing the generation of false alarms is a major challenge in any IDS implementation. Additionally, the sheer number of IoT devices that generate alarms in a moderately large sensor network may be overwhelming. In order to reduce alarms, this paper contributes to the field by proposing an original framework that limits the number of generated messages without compromising detection accuracy. The primary idea is to exploit mid-level nodes called collectors where similar alerts are collected and analyzed independently. Priority is assigned to each alert and similar alerts are fused to respective collectors for more informed decision making. In addition, Kademlia based Distributed Hash Table (DHT) is used for efficient alert transportation and distributed fusion of similar alerts. In order to minimize false alarm rate, event correlation is used to find similarity between events fused by different detection sensors. The framework is implemented in a fog-based environment to assess and evaluate the efficiency of the proposed system in edge network. The architecture is evaluated with the recognized DARPA 1999 dataset; the reported results show that the proposed technique reduces message generation by 62% while achieving false positive accuracy over 80%.

Designing Optimal Path for Wireless Sensor Networks by Combining Energy and Security Components.

The data transferring using multi-directional in remote sensor systems (WSNs) offers little security against malicious attacks through proper acknowledgement. An enemy can use confidential information to attack events, also deteriorate proper functioning of routing protocols. This situation is also expands to mobile and hostile network conditions. In this novel, I would like to propose trustworthy and operational location based routing instructions which provides security and also helpful to extends this works for large wireless sensor networks. The proposed idea has been clearly provide the mechanism to finds out malicious attacks as well as to provide security. As per the statistics, There are constraints on storage, processing of data, battery resources and variation in frequency ranges deeply effects for implementation, in addition to this data propagation ,improper links between network components, requires extra care while choosing different routing paths.

Distributed Intra and Inter Cluster Chaining Framework for Energy-Efficient, Delay Bounded and Scalable Data Gathering Applications in Large Scale Sensor Network

This paper proposes a scalable, energy-efficient and scalable, energy efficient, delay bounded intra and inter cluster routing framework viz. GIICCF (Generalized Intra and Inter cluster chaining framework) for efficient data gathering in large scale wireless sensor networks. This approach extricates the benefits of both pure chain-based as well as pure cluster-based data gathering schemes in large scale Wireless Sensor Network (WSNs) without undermining with the drawbacks. GIICCF defines a localized energy-efficient chaining scheme among the member nodes within the cluster with bounded data delivery delay to the respective cluster-heads (CH) as well as enables the CH to deliver data to the Base station (BS) following an energy-efficient multi-hop fashion. Detailed experimental analysis and simulation results reveal GIICCF increases the performance of any pure cluster-based and chain-based protocols by a huge margin.

Reliability and Energy Efficiency of DEAR Protocol with Cooperative Caching in IEEE802.15.4 based large ubiquitous Wireless Sensor Networks

Reliability and Energy Consumption issues in large ubiquitous Wireless Sensor Networks are a cause of concern especially because there is an inherent conflict between the two: an increase in reliability usually leads to an increase in energy consumption. Conversely, energy conservation has been a priority research concern in wireless sensor nodes. Data aggregation from various nodes and its transmission to the sink node through multiple hops which is important for network reliability increases the overall energy consumption in the network. Several schemes were proposed in the past to address the reliability needs and also to minimize the energy consumption in the network. In this context, this paper proposes a novel strategy for IEEE802.15.4/ZigBee based networks by incorporating a Distributed Energy Aware Routing (DEAR) protocol with a localized Cooperative Caching algorithm that addresses the query generated by a requester node or sink node with datum already existing in the locally available cache memory or in the memory of its one-hop neighbors or by the source node. The DEAR protocol considers battery level as a key factor to include nodes in its routing path. The proposed model is evaluated on the basis of three scenarios which were considered to illustrate the impact of energy consumption on the reliability of WSNs.

A Novel Hybrid Optimization Scheme on Connectivity Restoration Processes for Large Scale Industrial Wireless Sensor and Actuator Networks

In the wireless sensor and actuator networks (WSANs) of industrial field monitoring, maintaining network connectivity with coverage perception plays a decisive role in many industrial process scenarios. The mobile actuator node is responsible for collecting data from the sensing nodes and performing diverse specific collaborative operation tasks. However, the failure of the nodes usually causes coverage vulnerability and partition of the network. Urgent and time-sensitive applications expect a minimum coverage loss to complete an instant connectivity restoration. This paper presents a hybrid coverage perception-based connectivity restoration algorithm, which is designed to restore network connectivity with minimal coverage area loss. The algorithm uses a backup node, which is selected nearby the critical node, to ensure a timely restoration when the critical node encounters failure. In the process of backup node migration, the optimal destination will be reselected to maintain the best network coverage after network connectivity recovery. The effectiveness of the proposed algorithm was verified by some simulation experiments.

On the topology design of large wireless sensor networks for distributed consensus with low power consumption

Sensor-based structural health monitoring systems are commonly used to provide real-time information and detect damage in complex structures. In particular, wireless structural health monitoring systems are of low cost but, since wireless sensors are powered with batteries, a low power consumption is critical. A common approach for wireless structural health monitoring is to use a distributed computation strategy, which is usually based on consensus algorithms. Power consumption in such wireless consensus networks depends on the number of connections of the network. If sensors are randomly connected, there is no control on the power consumption. In this article, we present a novel strategy to connect a large number of wireless sensors for distributed consensus with low power consumption by combining small networks with basic topologies using the Kronecker product.

Energy Efficient Clustering and Congestion Control in WSNs with Mobile Sinks

Large scale Wireless Sensor Networks (WSNs) often utilize multiple mobile sink nodes to improve the network lifetime and scalability. However, most of the studies conducted in this context, consider unlimited buffer capacity for the sink nodes. But, this model cannot truly describe the behavior of WSNs and causes congestion in the sink nodes. To solve this problem, in this paper, we use limited buffer capacity for each mobile sink node in WSNs and present a two-level Fuzzy Logic Controller (FLC)-based dynamic clustering scheme and congestion prevention. In this scheme, sink nodes try to predict current load based on their loads in previous rounds by using ARIMA method and based on it, the first FLC selects the nearest uncongested sink node from multiple available mobile sink nodes. Then, the second FLC applies the output of the first FLC to select appropriate nodes as cluster heads to mitigate the energy consumption in the network. Extensive simulation results indicate the effectiveness of the proposed fuzzy logic-based solution in reducing congestion in the mobile sink nodes and improving load balancing in them which these result in the network lifetime improvement and decreasing the number of retransmissions.

LBCSM: Load Balanced Constant Scheduling Model for Node Recharging in Wireless Rechargeable Sensor Networks using Multiple Mobile Charging Devices

In large-scale Wireless Rechargeable Sensor Networks (WRSNs), limited battery capacity of nodes may reduce the network longevity. For enhancing the network lifetime, the nodes in the network can recharged periodically based on their operational executions. The rechargeable sensor nodes in the network are replenished using external sources. Using single charging device can be feasible only for small scale WSNs, whereas in managing large scale wireless sensor networks, multiple charging devices are to be modelled for efficiently recharging the sensor nodes, since single devices are having energy constraints to recharge more number of nodes. On focussing those issues, this paper contributes on developing a new model called Load Balanced Constant Scheduling (LBCS) for the replenishment of the sensor nodes. Moreover, multiple Mobile Charging Devices (MCDs) are used here for recharging the sensor nodes effectively, without facing resource limitations. In this model, constant and time based charge scheduling approach and charging route for MCD has been frame optimally. The scheduling mode focuses on a concrete classification procedure for avoiding needless visits of nodes having adequate energy. Providing further improvement in schedule based node replenishment, algorithm for Charging Route Definition (CRD) is also developed in this work. For evidencing the efficiency of the proposed model, the work is simulated and evaluated. The simulation results are compared with some existing models based on the network lifetime, time taken for recharge and efficiency.

A GRASP Meta-Heuristic for Evaluating the Latency and Lifetime Impact of Critical Nodes in Large Wireless Sensor Networks

Wireless Sensor Networks (WSN) have lately been gaining momentum thanks to the hardware improvements and standardization software efforts. Moreover, the appearance of Internet of Things (IoT) and its reliance on sensors are helping to widely extend the usage of WSNs. However, such networks present drawbacks, mainly because of limited sensor batteries and their vulnerability against physical attacks due to the lack of protection and security. Additionally, not all the sensors inside the network have the same responsibility in terms of traffic handling. In this paper, we firstly analyze the fact that some nodes are more critical than others, considering the most critical node the one that, once incapacitated, causes the most deterioration on the network performance. Such performance is analyzed using two metrics, namely network latency and lifetime. We present a result comparison between a Mixed Integer Programming (MIP) model and a Greedy Randomized Adaptive Search Procedure (GRASP) meta-heuristic for small networks. For bigger networks, GRASP meta-heuristic results are presented to understand how the network degrades as the number of both critical and network nodes increase, by distributing them into two different areas: fixed and incremental to maintain node density.

Speed prediction in large and dynamic traffic sensor networks

Smart cities are nowadays equipped with pervasive networks of sensors that monitor traffic in real-time and record huge volumes of traffic data. These datasets constitute a rich source of information that can be used to extract knowledge useful for municipalities and citizens. In this paper we are interested in exploiting such data to estimate future speed in traffic sensor networks, as accurate predictions have the potential to enhance decision making capabilities of traffic management systems. Building effective speed prediction models in large cities poses important challenges that stem from the complexity of traffic patterns, the number of traffic sensors typically deployed, and the evolving nature of sensor networks. Indeed, sensors are frequently added to monitor new road segments or replaced/removed due to different reasons (e.g., maintenance). Exploiting a large number of sensors for effective speed prediction thus requires smart solutions to collect vast volumes of data and train effective prediction models. Furthermore, the dynamic nature of real-world sensor networks calls for solutions that are resilient not only to changes in traffic behavior, but also to changes in the network structure, where the cold start problem represents an important challenge. We study three different approaches in the context of large and dynamic sensor networks: local, global, and cluster-based. The local approach builds a specific prediction model for each sensor of the network. Conversely, the global approach builds a single prediction model for the whole sensor network. Finally, the cluster-based approach groups sensors into homogeneous clusters and generates a model for each cluster. We provide a large dataset, generated from ∼1.3 billion records collected by up to 272 sensors deployed in Fortaleza, Brazil, and use it to experimentally assess the effectiveness and resilience of prediction models built according to the three aforementioned approaches. The results show that the global and cluster-based approaches provide very accurate prediction models that prove to be robust to changes in traffic behavior and in the structure of sensor networks.

Design and Performance Analysis of Secure Multicasting Cooperative Protocol for Wireless Sensor Network Applications

This letter proposes a new security cooperative protocol, for dual phase amplify-and-forward large wireless sensor networks. In such a network, a portion of the ${K}$ relays can be potential eavesdroppers. The source agrees to share with the destination a given channel state information (CSI) of a source-trusted relay-destination link to encode the message. Then, in the first hop, the source will use this CSI to map the right message to a certain sector while transmitting fake messages to the other sectors using sectoral transmission. In the second hop, the relays retransmit their received signals to the destination, using the distributed beamforming technique. We derived the secrecy outage probability and demonstrated that the probability of receiving the right encoded information by an untrustworthy relay is inversely proportional to the number of sectors. We also showed that the aggressive behavior of the cooperating untrusted relays is not effective compared to the case where each untrusted relay is trying to intercept the transmitted message individually.

Fusion Rules for Distributed Detection in Clustered Wireless Sensor Networks With Imperfect Channels

In this paper, we investigate fusion rules for distributed detection in large random clustered wireless sensor networks with a three-tier hierarchy; the sensor nodes (SNs), the cluster heads (CHs), and the fusion center (FC). The CHs collect the SNs’ local decisions and relay them to the FC, which then fuses them to reach the ultimate decision. The SN–CH and the CH–FC channels suffer from additive white Gaussian noise. In this context, we derive the optimal log-likelihood ratio (LLR) fusion rule, which turns out to be intractable. So, we develop a sub-optimal linear fusion rule (LFR) that weighs the cluster's data according to both its local detection performance and the quality of the communication channels. In order to implement it, we propose an approximate maximum likelihood based LFR (LFR-aML), which estimates the required parameters for the LFR. We also derive Gaussian-tail upper bounds for the detection and false alarms probabilities for the LFR. Furthermore, an optimal CH transmission power allocation strategy is developed by solving the Karush–Kuhn–Tucker conditions for the related optimization problem. Extensive simulations show that the LFR attains a detection performance near to that of the optimal LLR and confirms the validity of the proposed upper bounds. Moreover, when compared to equal power allocation, simulations show that our proposed power allocation strategy achieves a significant power saving at the expense of a small reduction in the detection performance.

Recent progress in radon-based monitoring as seismic and volcanic precursor: A critical review

Prediction of earthquakes and volcanic eruptions has concerned researchers for many decades. Several precursors (e.g. seismic, geodetic, geochemical, geological, atmospheric/ionospheric, geomagnetic, electrical) have been observed shortly before an earthquake or volcanic event. However, no precursor, that can accurately be used for forecasting, has yet been discovered due to the involvement of several complex overlapping and interacting physical and chemical processes. In particular, the non-linearity of actual eruptions or quakes implies a high statistical uncertainty about location of measurement devices. Among geochemical precursors, radon gas in groundwater and soil is considered a notable precursor, used to detect chemical and physical changes during the generation of earthquakes and volcanic events. This article critically reviews progress in radon-based monitoring from the year 2000 onwards and catalogs anomalous radon variations found in groundwater and soils. A future deployment of large sensor networks of 1000–10,000 detectors for radon and also thoron detection would bring a shift in paradigm with respect to long-term earthquake and volcanic monitoring. Such a dense network would enable rapid and precise measurements of radon over large areas resulting in establishing significant and relevant statistical data.

On the Realistic Radio and Network Planning of IoT Sensor Networks.

Planning and deploying a functional large scale Wireless Sensor Network (WSN) or a Network of Internet of Things (IoTs) is a challenging task, especially in complex urban environments. A main network design bottleneck is the existence and/or correct usage of appropriate cross layer simulators that can generate realistic results for the scenario of interest. Existing network simulators tend to overlook the complexity of the physical radio propagation layer and consequently do not realistically simulate the main radio propagation conditions that take place in urban or suburban environments, thus passing inaccurate results between Open Systems Interconnection (OSI) layers. This work demonstrates through simulations and measurements that, by correctly passing physical information to higher layers, the overall simulation process produces more accurate results at the network layer. It is demonstrated that the resulting simulation methodology can be utilized to accomplish realistic wireless planning and performance analysis of the deployed nodes, with results that are very close to those of real test-beds, or actual WSN deployments.

AREOR–Adaptive ranking based energy efficient opportunistic routing scheme in Wireless Sensor Network

Energy efficient and cluster based routing techniques are gaining popularity due to complexities associated with the power and maintenance factors of large Wireless Sensor Networks during transmission. An adaptive ranking based energy efficient opportunistic routing protocol- AREOR is researched in this paper. It identifies the most effective node to participate as a cluster head by using adaptive participatory criteria. A novel forwarder node selection method, based on adaptive ranking system is proposed. Node’s remaining energy and geographic position is utilized to compute the ranks. The proposed approach is verified through variable sized clustered networks to investigate the impact of opportunistic routing. Evaluation parameters such as Message Success Rate, Energy Consumption, End-to-End delay and Packet Delivery Ratio are used to demonstrate the effectiveness of the proposed energy optimization approach. AREOR reports reduction in energy consumption for the duration of the transmission. Adaptive ranking and energy optimal forwarder node selection has shown an edge over competitive peer techniques as per demonstrative simulations performed on realistic scenarios.

A load balancing virtual level routing (LBVLR) using mobile mule for large sensor networks

In a large sensor network, the data are usually routed back to the static base station (BS) using multi-hop communication. The sensor nodes near the BS suffer from an energy hole problem owing to the heavier data traffic load on them. Hence, the nodes near to the BS die early and thereby minimize the network lifetime. The mobile carriers have been considered as a better approach to balance the energy consumption by minimizing the number of transmissions. In spite of several benefits, the mobile carriers have a lot of challenges in data collection from sensor nodes. This paper presents a load balancing virtual level-based routing (LBVLR) using a data mule as a mobile carrier to deal with energy holes and ensure reliable data transfer. Our scheme has attempted to palliate the energy hole problem by applying horizontal level routing in the left and right directions. Fixed virtual rectangular grids of equal size are formed with the help of a mobile data mule and the BS that minimizes the network overheads, which saves a significant amount of energy. This approach uses three different data collection schemes based on the mule speed during trajectory, and we name them uniform speed of mule, variable speed of mule and sojourn point of mule. To compare the performance with the other works, LBVLR is evaluated mathematically and extensive simulations are conducted. The simulation results show a significant improvement in terms of average energy usage, data delivery delay and data delivery ratio compared with the other two similar kinds of well-known routing protocols.

Biologically Inspired Low Energy Clustering for Large Scale Wireless Sensor Networks

The recent technological advances in data gathering, embedded micro-devices and mobile networking has significantly advanced the applications of small-size and numerous tiny nodes. Large scale wireless sensor networks (LSWSNs) are intensively studied and used in the fields of traffic avoidance, intelligent family, medical diagnostic, environmental, multimedia surveillance, military affairs and so on. The recent success of emerging LSWSNs technology has encouraged researchers to develop new low energy clustering algorithm in this field. In LSWSNs, reducing communication energy consumption of sensor will not lead to maximize network lifetime for the total system. The low energy clustering is a typical NP-hard combinatorial optimization problem. In this paper, an immune adaptive cuckoo search algorithm (IACSA) is given to reduce total energy consumption. We first design a fitness function to evaluate energy consumption of system. The IACSA is designed to improve the energy efficiency for LSWSNs. It has the advantages of immune generator that takes into account different benefits and adaptive operator to enhance the convergence rate. Simulations are conducted to show a comparison of IACSA with the shuffled frog leaping algorithm (SFLA), particle swarm optimization (PSO) and artificial fish swarm algorithm (AFSA). Results show that the proposed IACSA has lower energy consumption compared to the SFLA, AFSA and PSO, which means that the proposed method reduces the energy consumption.

Magnetostrictive Energy Harvesting: Materials and Design Study

In recent years, vibrational energy harvesting has established itself as a promising alternative to the use of batteries for powering microelectromechanical systems for large wireless sensor networks used in aerospace and building infrastructures. This paper has focused on the design and materials used in magnetostrictive cantilever energy harvesters. The study involved using both finite-element modeling to predict the resonance frequencies for different cantilever designs and magnetostrictive materials, followed by experimental measurements for validation. Two different magnetostrictive ribbons were investigated, Fe100– x Ga x with four different compositions ( $x = 17.5$ ; 19.5; 21; 28 at.%) and amorphous metallic glass Metglas 2605SC (Fe81B13.5Si3.5C2). From the modeling, it was determined that the resonance frequency was strongly dependent on the cantilever length, thickness, and density. Changing the cantilever design to a “T” shape was found to decrease the resonance frequency. The experimental results found that the output voltage measured depended on the cantilever dimensions, especially the thickness, the Ga concentration, and the cantilever design. The output voltages for Fe80.5Ga19.5 cantilevers were comparable with the same dimension Metglas cantilevers. The results of the finite-element modeling were validated by good agreement between the computational and experimental resonance frequencies measured.