Internet of Things-based wireless sensor networks for monitoring access constraint security measures in liable data aggregation

A wireless sensor network (WSN) is a network of autonomous, small sensors that can detect, collect, and send data about their surrounding environment. In the Internet of Things (IoT) infrastructure, WSNs are the smart devices that provide the platform with resource input. Security breaches and insider attacks are possible due to the WSN's resource-constrained design. However, the IoT platform's intelligence may be extended to WSN nodes for managing device and data-level security. This paper proposes Monitored Access Constraint Security (MACS) to ensure the privacy of data collected via the ubiquitous processing enabled by the Internet of Things. The IoT platform performs frequent checks on the quality of the interactions between the various nodes to ensure that they are functioning properly and that the sensor aggregation instances are accountable. Node liability is considered while adjusting the aggregate level and the continuity. The method guarantees secure information from the environment and the data sources. The quality of the data gathered in the suggested technique is evaluated based on node liability and information extraction feature. Accordingly, security measures are implemented at data gathering and filtering levels and then assessed using a recurrent learning process. Since there are fewer security breaches overall, the rate of aggregation increases. Aggregation loss, delay time, false rate, throughput, and verification time are used to evaluate the performance.     

Modulated relay based stable election protocol for large-scale wireless sensor networks

Internet of things (IoT) applications based on wireless sensor networks (WSNs) have recently gained vast momentum. These applications vary from health care, smart cities, and military applications to environmental monitoring and disaster prevention. As a result, energy consumption and network lifetime have become the most critical research area of WSNs. Through energy-efficient routing protocols, it is possible to reduce energy consumption and extend the network lifetime for WSNs. Using hybrid routing protocols that incorporate multiple transmission methods is an effective way to improve network performance. This paper proposes modulated R-SEP (MR-SEP) for large-scale WSN-based IoT applications. MR-SEP is based on the well-known stable election protocol (SEP). MR-SEP defines three initial energy levels for the nodes to improve the network energy distribution and establishes multi-hop communication between the cluster heads (CHs) and the base station (BS) through relay nodes (RNs) to reduce the energy consumption of the nodes to reach the BS. In addition, MR-SEP reduces the replacement frequency of CHs, which helps increase network lifetime and decrease power consumption. Simulation results show that MR-SEP outperforms SEP, LEACH, and DEEC protocols by 70.2%, 71.58%, and 74.3%, respectively, in terms of lifetime and by 86.53%, 86.68%, and 86.93% in terms of throughput.     

DR-NAP: Data reduction strategy using neural adaptation phenomenon in wireless sensor networks

Internet of Things (IoT) has got significant popularity among the researchers' community as they have been applied in numerous application domains. Most of the IoT applications are implemented with the help of wireless sensor networks (WSNs). These WSNs use different sensor nodes with a limited battery power supply. Hence, the energy of the sensor node is considered as one of the primary constraints of WSN. Besides, data communication in WSN dissipates more energy than processing the data. In most WSNs applications, the sensed data generated from the same location sensor nodes are identical or time-series/periodical data. This redundant data transmission leads to more energy consumption. To reduce the energy consumption, a data reduction strategy using neural adaptation phenomenon (DR-NAP) has been proposed to decrease the communication energy in routing data to the BS in WSN. The neural adaptation phenomenon has been utilized for designing a simple data reduction scheme to decrease the amount of data transmitted. In this way, the sensor node energy is saved and the lifetime of the network is enhanced. The proposed approach has been implanted in the existing gravitational search algorithm (GSA)-based clustered routing for WSN. The sensed data are transmitted to CH and BS using DR-NAP. Real sensor data from the Intel Berkeley Research lab have been used for conducting the experiments. The experiment results show 47.82% and 51.96% of improvement in network lifetime when compared with GSA-based clustered routing and clustering scheme using Canada Geese Migration Principle (CS-CGMP) for routing, respectively.     

An efficient energy consumption model using data aggregation for wireless sensor network

Wireless sensor networks (WSNs) have become increasingly important in recent years. Small and low-power sensor nodes make up these sensor networks. A random distribution of nodes is made throughout an unmanaged target region. One of WSN's key challenges is its limited and irreplaceable energy supply. In most situations, sensor nodes cannot be replaced since they operate in a hostile physical environment. The act of gathering and aggregating usable data from different sensor nodes situated to perceive almost the same attribute of the occurrence is known as data aggregation. The mathematical model is used in this research study to generate cluster-based data aggregation, which is an effective technique to increase energy usage by minimising the number of data transfers. The proposed mathematical model-based data aggregation (MM-DA) attains a 97% packet delivery ratio with minimal energy consumption. The MM-DA outperforms other existing approaches in terms of packet delivery ratio (PDR), energy consumption (EC), network lifetime and control overhead.     

A new load balancing and data collection algorithm for energy saving in wireless sensor networks

Data gathering is a major function of many applications in wireless sensor networks. The most important issue in designing a data gathering algorithm is how to save energy of sensor nodes while meeting the requirements of special applications or users. Wireless sensor networks are characterized by centralized data gathering, multi-hop communication and many to one traffic pattern. These three characteristics can lead to severe packet collision, network congestion and packet loss, and even result in hot-spots of energy consumption thus causing premature death of sensor nodes and entire network. In this paper, we propose a load balance data gathering algorithm that classifies sensor nodes into different layers according to their distance to sink node and furthermore, divides the sense zone into several clusters. Routing trees are established between sensor node and sink depending on the energy metric and communication cost. For saving energy consumption, the target of data aggregation scheme is adopted as well. Analysis and simulation results show that the algorithm we proposed provides more uniform energy consumption among sensor nodes and can prolong the lifetime of sensor networks.     

Sandpiper optimization algorithm with cosine similarity based cross-layer routing protocol for smart agriculture in wireless sensor network assisted internet of things systems

The Internet of Things (IoT) has recently attained a prominent role in enabling smooth and effective communication among various networks. Wireless sensor network (WSN) is utilized in IoT to collect peculiar data without interacting with humans in specific applications. Energy is a major problem in WSN-assisted IoT applications, even though better data communication is achieved through cross-layer models. This paper proposes a new cross-layer-based clustering and routing model to provide a scalable and energy-efficient long data communication in WSN-assisted IoT systems for smart agriculture. Initially, the fuzzy k-medoids clustering approach is used to split the network into various clusters since the formation of clusters plays an important role in energy consumption. Then, a new swarm optimization known as enhanced sparrow search algorithm (ESSA), which is the combination of SSA and chameleon swarm algorithm (CSA), has been introduced for optimal cluster head (CH) selection to solve the energy-hole problems in WSN. A cross-layer strategy has been preferred to provide efficient data transmission. Each sensor node parameter of the physical layer, network layer and medium access control (MAC) is considered for processing routing. Finally, a new bio-inspired algorithm is known as the sandpiper optimization algorithm (SOA), and cosine similarity (CS) has been employed to determine the optimal route for efficient data transmission and retransmission. The simulation of the proposed protocol is implemented by network simulator (NS2), and the simulation results are taken in terms of end-to-end delay, PDR, communication overhead, communication cost, average consumed energy, and network lifetime.     

An application of meta-heuristic and nature-inspired algorithms for designing reliable networks based on the Internet of things: A systematic literature review

The widespread use of Internet of Things (IoT) in various wireless sensor networks applications has increased their importance in recent years. IoT is a smart technology that connects anything anywhere at any time. These smart objects, which connect the physical world with the world of computing infrastructure, are expected to permeate all aspects of our daily lives and revolutionize a number of application domains such as healthcare, energy conservation, and transportation. As wireless networking expands, the disadvantage of wireless communication is clearly obvious. People's apprehension over the IoT's dependability has therefore skyrocketed. IoT networks' key requirements are dependability, channel security, fault tolerance, and reliability. Monitoring the IoT networks depends on the availability and correct functioning of all the network nodes. Recent research has proposed promising solutions to address these challenges. This article systematically examines recent articles that use meta-heuristic and nature-inspired algorithms to establish reliable IoT networks. Eighteen articles were analyzed in four groups. Results showed that reliable enhancement mechanisms in IoT networks increase fault node detection, network efficiency, and lifetime and attain energy optimization results in the IoT concept. Additionally, it was discovered in the literature that the current studies focus on how to effectively use edge network capabilities for IoT application executions and support, along with the related needs.     

Dynamic clustering approach based on wireless sensor networks genetic algorithm for IoT applications

Energy is vital parameter for communication in Internet of Things (IoT) applications via Wireless Sensor Networks (WSN). Genetic algorithms with dynamic clustering approach are supposed to be very effective technique in conserving energy during the process of network planning and designing for IoT. Dynamic clustering recognizes the cluster head (CH) with higher energy for the data transmission in the network. In this paper, various applications, like smart transportation, smart grid, and smart cities, are discussed to establish that implementation of dynamic clustering computing-based IoT can support real-world applications in an efficient way. In the proposed approach, the dynamic clustering-based methodology and frame relay nodes (RN) are improved to elect the most preferred sensor node (SN) amidst the nodes in cluster. For this purpose, a Genetic Analysis approach is used. The simulations demonstrate that the proposed technique overcomes the dynamic clustering relay node (DCRN) clustering algorithm in terms of slot utilization, throughput and standard deviation in data transmission.

     

Data Transmission Using IoT in Vehicular Ad-Hoc Networks in Smart City Congestion

Development of Internet of Things (IoT) enables smart city advancement throughout the world. Increasing number of vehicles has brought focus on road safety precautions and in-vehicle communication. This is the right time to focus on the development of new applications and services for vehicular environments. The Vehicular Ad-hoc Networks (VANETs) are an interesting range of Mobile Ad-hoc Networks (MANETs) where the Vehicle to Vehicle (V2V) and vehicle roadways transmission is possible. The V2V scheme is fresh by combining Wireless Fidelity (Wi-Fi), Bluetooth and other all sorts of communication standards. An immense number of nodes working with these networks and due to their immense displacements, the analysis is prevailing regarding the possibility of routing standards. The estimation of conventional routing standards for MANETs illustrates that their behaviors are minimal in VANETs. The intention is to make use of mediators for routing with an effort to address the before described issues. The mediators are accountable for gathering data related to routing and identifying the optimal paths for forwarding information packets. The routing scheme is based on group routing standards and data cluster framework for locating the best possible routes. In this paper, we analyze smart cities vehicle communication development by implementing IoT. We also discuss the ways to minimize the limitations connected to IoT deployment and implementation in smart city environment using multi mediator scheme.

     

Topology Management Ensuring Reliability in Delay Sensitive Sensor Networks with Arbitrary Node Failures

The lifetime of a sensor network is influenced by the efficient utilization of the resource constrained sensor nodes. The tree-based data gathering offers good quality of service (QoS) for the running applications. However, data gathering at the sink reduces the network lifetime due to a fast failure of highly loaded nodes. Loss of connectivity and sensing coverage affect the performance of the applications that demand critical QoS. In this paper, a data gathering tree management scheme has been proposed to deal with arbitrary node failures in delay-sensitive sensor networks. A load-balanced distributed BFS tree construction procedure has been introduced for an efficient data gathering. Based on the initial tree construction, a tree maintenance scheme and an application message handler have been designed to ensure the reliable delivery of the application messages. The correctness of the proposed scheme has been verified both theoretically and with the help of simulation. The proposed scheme offers low overhead, enhanced network lifetime and good QoS in terms of delay and reliability of the application messages.     

在传感器网络中构造延迟限定的最大化生命周期树

在一些对延迟敏感的持续性监视应用中,无线传感器网络中的数据收集需要构造延迟限定的最大化生命周期树,这属于NP完全问题。提出一个新的算法MILD,通过限定树的高度来满足延迟限定,然后通过使树上“瓶颈节点”的度最小化来延长树的生命周期。实验表明,与目前已有的协议相比,MILD能有效地限定延迟并延长树的生命周期。     

Structure-Free Data Aggregation in Sensor Networks

Data aggregation protocols can reduce the communication cost, thereby extending the lifetime of sensor networks. Prior works on data aggregation protocols have focused on tree-based or cluster-based structured approaches. Although structured approaches are suited for data gathering applications, they incur high maintenance overhead in dynamic scenarios for event-based applications. The goal of our work is to design techniques and protocols that lead to efficient data aggregation without explicit maintenance of a structure. As packets need to converge spatially and temporally for data aggregation, we propose two corresponding mechanisms - data-aware anycast at the MAC layer and randomized waiting at the application layer. We model the performance of the combined protocol that uses both the approaches and show that our analysis matches with the simulations. Using extensive simulations and experiments on a testbed with implementation in TinyOS, we study the performance and potential of structure-free data aggregation.     

PRDA: polynomial regression‐based privacy‐preserving data aggregation for wireless sensor networks

In wireless sensor networks, data aggregation protocols are used to prolong the network lifetime. However, the problem of how to perform data aggregation while preserving data privacy is challenging. This paper presents a polynomial regression‐based data aggregation protocol that preserves the privacy of sensor data. In the proposed protocol, sensor nodes represent their data as polynomial functions to reduce the amount of data transmission. In order to protect data privacy, sensor nodes secretly send coefficients of the polynomial functions to data aggregators instead of their original data. Data aggregation is performed on the basis of the concealed polynomial coefficients, and the base station is able to extract a good approximation of the network data from the aggregation result. The security analysis and simulation results show that the proposed scheme is able to reduce the amount of data transmission in the network while preserving data privacy. Copyright © 2013 John Wiley & Sons, Ltd.     

物联网中音频事件检测方法的研究

Recently, the Internet of Things (IoT) has attracted more and more attention. Multimedia sensor network plays an important role in the IoT, and audio event detection in the multimedia sensor networks is one of the most important applications for the Internet of Things. In practice, it is hard to get enough real-world samples to generate the classifiers for some special audio events (e.g., car-crashing in the smart traffic system). In this paper, we introduce a TrAdaBoost-based method to solve the above problem. By using the proposed approach, we can train a strong classifier by using only a tiny amount of real-world data and a large number of more easily colle cted samples (e.g., collected from TV programs), even when the real-world data is not sufficient to train a model alone. We deploy this approach in a smart traffic system to evaluate its performance, and the experiment evaluations demonstrate that our method can achieve satisfying results.     

Security and Privacy in IoT: A Survey

The Internet of Things (IoT) is a network of globally connected physical objects, which are associated with each other via Internet. The IoT foresees the interconnection of few trillions of intelligent objects around us, uniquely and addressable every day, these objects have the ability to accumulate process and communicate data about themselves and their surrounding environment. The best examples of IoT systems are health care, building smart city with advance construction management system, public and defense surveillance and data acquisition. Recent advancement in the technology has developed smart and intelligent sensor nodes and RFIDs lead to a large number of wireless networks with smart and intelligent devices (object, or things) connected to the Internet continuously transmit the data. So to provide security and privacy to this data in IoT is a very challenging task, which is to be concerned at highest priority for several current and future applications of IoT. Devices such as smart phone, WSNs and RFIDs etc., are the major components of IoT network which are basically resource constrained devices. Design and development of security and privacy management schemes for these devices is guided by factors like good performance, low power consumption, robustness to attacks, tampering of the data and end to end security. Security schemes in IoT provide unauthorized access to information or other objects by protecting against alterations or destruction. Privacy schemes maintain the right to control about the collected information for its usage and purpose. In this paper, we have surveyed major challenges such as Confidentiality, Integrity, Authentication, and Availability for IoT in a brief manner.

     

A systematic literature review of machine learning applications in IoT

The Internet of Things (IoT) is a network of interconnected smart objects having capabilities that collectively form an ecosystem and enable the delivery of smart services to users. The IoT is providing several benefits into people's lives through the environment. The various applications that are run in the IoT environment offer facilities and services. The most crucial services provided by IoT applications are quick decision for efficient management. Recently, machine learning (ML) techniques have been successfully used to maximize the potential of IoT systems. This paper presents a systematic review of the literature on the integration of ML methods in the IoT. The challenges of IoT systems are split into two categories: fundamental operation and performance. We also look at how ML is assisting in the resolution of fundamental system operation challenges such as security, big data, clustering, routing, and data aggregation.     

BECPA: Bandwidth Efficient Cluster Based Packet Aggregation in Wireless Sensor Network

In recent years, energy consumption and data gathering is a foremost concern in many applications of wireless sensor networks (WSNs). The major issue in WSNs is effective utilization of the resource as energy and bandwidth with a large gathering of data from the monitoring and control applications. This paper proposes novel Bandwidth Efficient Cluster based Packet Aggregation algorithm for heterogeneous WSNs. It combines the idea of variable packet generation rate of each node with random data. The nodes are randomly distributed with different energy level and are equal in numbers. It uses the perfectly compressible aggregation function at cluster head based on the correlation of packets and data generated by each node. Compare to state-of-the-art solutions, the algorithm shows 4.43 % energy savings with reduced packet delivery ratio (62.62 %) at the sink. It shows better bandwidth utilization in packet aggregation than data aggregation.     

Aggregate node placement for maximizing network lifetime in sensor networks

Sensor networks have been receiving significant attention due to their potential applications in environmental monitoring and surveillance domains. In this paper, we consider the design issue of sensor networks by placing a few powerful aggregate nodes into a dense sensor network such that the network lifetime is significantly prolonged when performing data gathering. Specifically, given K aggregate nodes and a dense sensor network consisting of n sensors with K ≪ n, the problem is to place the K aggregate nodes into the network such that the lifetime of the resulting network is maximized, subject to the distortion constraints that both the maximum transmission range of an aggregate node and the maximum transmission delay between an aggregate node and its covered sensor are met. This problem is a joint optimization problem of aggregate node placement and the communication structure, which is NP‐hard. In this paper, we first give a non‐linear programming solution for it. We then devise a novel heuristic algorithm. We finally conduct experiments by simulation to evaluate the performance of the proposed algorithm in terms of network lifetime. The experimental results show that the proposed algorithm outperforms a commonly used uniform placement schema — equal distance placement schema significantly. Copyright © 2010 John Wiley & Sons, Ltd.     

PFARS: Enhancing throughput and lifetime of heterogeneous WSNs through power‐aware fusion,aggregation, and routing scheme

Heterogeneous wireless sensor networks (WSNs) consist of resource‐starving nodes that face a challenging task of handling various issues such as data redundancy, data fusion, congestion control, and energy efficiency. In these networks, data fusion algorithms process the raw data generated by a sensor node in an energy‐efficient manner to reduce redundancy, improve accuracy, and enhance the network lifetime. In literature, these issues are addressed individually, and most of the proposed solutions are either application‐specific or too complex that make their implementation unrealistic, specifically, in a resource‐constrained environment. In this paper, we propose a novel node‐level data fusion algorithm for heterogeneous WSNs to detect noisy data and replace them with highly refined data. To minimize the amount of transmitted data, a hybrid data aggregation algorithm is proposed that performs in‐network processing while preserving the reliability of gathered data. This combination of data fusion and data aggregation algorithms effectively handle the aforementioned issues by ensuring an efficient utilization of the available resources. Apart from fusion and aggregation, a biased traffic distribution algorithm is introduced that considerably increases the overall lifetime of heterogeneous WSNs. The proposed algorithm performs the tedious task of traffic distribution according to the network's statistics, ie, the residual energy of neighboring nodes and their importance from a network's connectivity perspective. All our proposed algorithms were tested on a real‐time dataset obtained through our deployed heterogeneous WSN in an orange orchard and also on publicly available benchmark datasets. Experimental results verify that our proposed algorithms outperform the existing approaches in terms of various performance metrics such as throughput, lifetime, data accuracy, computational time, and delay.