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.

     

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.     

Coverage and Connectivity-Based 3D Wireless Sensor Deployment Optimization

The wireless sensor network technology of Internet of Things (IoT) senses, collects and processes the data from its interconnected intelligent sensors to the base station. These sensors help the IoT to understand the environmental change and respond towards it. Thus sensor placement is a crucial device of IoT for efficient coverage and connectivity in the network. Many existing works focus on optimal sensor placement for two dimensional terrain but in various real-time applications sensors are often deployed over three-dimensional ambience. Therefore, this paper proposes a vertex coloring based sensor deployment algorithm for 3D terrain to determine the sensor requirement and its optimal spot and to obtain 100% target coverage. Further, the quality of the connectivity of sensors in the network is determined using Breadth first search algorithm. The results obtained from the proposed algorithm reveal that it provides efficient coverage and connectivity when compared with the existing methods.

     

Green IoT: A Short Survey on Technical Evolution & Techniques

The Internet of Things (IoT) embodies the confluence of the virtual & physical world. IoT will play an important role in managing the managing depleting resource such as water, fuel, food, etc. However, to realize these applications enormous IoT devices will communicate with each other. This massive connectivity will directly or indirectly aid in Green House Gas emissions. Hence, to admissibly reduce this environmental impact of IoT, it must be greened in terms of energy consumption. Green IoT will reduce environmental exploitation by slashing carbon emission effectively and thus will help in achieving sustainability of the planet. This paper describes the journey of IoT to Green IoT. Along with this, the survey on recent Green-IoT techniques that will effectively help in reducing required energy consumption is presented. Along with this ability of unmanned aerial vehicle (UAV) technology to provide Green IoT and survey on recent energy-efficient UAV assisted communication is presented. In addition to this, a dual battery enabled Unmanned Aerial vehicle base station, an energy-efficient clustering algorithm, has also been proposed to prolong the battery life.

     

Edge Computing Offloading Strategy Based on Dynamic Non-cooperative Games in D-IoT

With the vigorous development of Internet of Things technology, the current distribution network is developing towards the information-based and intelligent distribution Internet of Things (D-IoT). D-IoT adopts the mode of the cloud computing center and the edge cloud network working together. The edge cloud network has a large number of intelligent terminals, which can well adapt to the current sharply expanding power data scale. In order to further improve the ability of the edge network in D-IoT to process data in real time, and to maximize the quality of user experience (QoE) while minimizing energy consumption when performing computing offload, this paper proposes a dynamic non-cooperative game based edge Computing task offloading strategy, considering the dynamic nature of task generation, designed a distributed iterative optimization algorithm, which decomposes computing offloading into a series of sub-problems to solve. The results of simulation experiments prove that the calculation offloading mechanism proposed in this paper can greatly improve D -Compute efficiency of IoT system.

     

An Efficient and Secure Data Forwarding Mechanism for Opportunistic IoT

Internet of Things (IoT) is a heterogeneous network of interconnected things where users, smart devices and wireless technologies, collude for providing services. It is expected that a great deal of devices will get connected to the Internet in the near future. Opportunistic networks(OppNet) are a class of disruption tolerant networks characterized by uncertain topology and intermittent connectivity between the nodes. Opportunistic Internet of Things(OppIoT) is an amalgamation of the OppNet and IoT exploiting the communication between the IoT devices and the communities formed by humans. The data is exposed to a wide unfamiliar audience and the message delivery is dependent on the residual battery of the node, as most of the energy is spent on node discovery and message transmission. In such a scenario where a huge number of devices are accommodated, a scalable, adaptable, inter-operable, energy-efficient and secure network architecture is required. This paper proposes a novel defense mechanism against black hole and packet fabrication attacks for OppIoT, GFRSA, A Green Forwarding ratio and RSA (Rivest, Shamir and Adleman) based secure routing protocol. The selection of the next hop is based on node’s forwarding behavior, current energy level and its predicted message delivery probability. For further enhancing the security provided by the protocol, the messages are encrypted using asymmetric cryptography before transmission. Simulations performed using opportunistic network environment (ONE) simulator convey that GFRSA provides message security, saves energy and outperforms the existing protocols, LPRF-MC (Location Prediction-based Forwarding for Routing using Markov Chain) and RSASec (Asymmetric RSA-based security approach) in terms of correct packet delivery by 27.37%, message delivery probability is higher by 34.51%, number of messages dropped are reduced by 15.17% and the residual node energy is higher by 14.08%.

     

Sling-shot spider optimization algorithm based packet length control in wireless sensor network and Internet of Things-based networks

Wireless sensor network (WSN) is a well-developed domain suitable in the optimal collection and processing of information needed for the present world. For processing and transmitting the correct data by the network, it is affected by many disrupting factors like interference, battery life, distance between the nodes, and redundant data. There have been many methods proposed in the past. Among all the disrupting factors, routing has been the most studied problem in the WSN literature, whereas packet length sizing has been the most untouched topic compared to routing. Sizing the packet lengths to transmit it through the network is important to get error-free data and run an energy-efficient network. In this paper, we propose a sling-shot spider optimization (S²SO) algorithm for packet length optimization in WSN and Internet of Things (IoT)-based networks. The S²SO algorithm is developed based on the spider's unique behavior of catching prey. The proposed algorithm is implemented in three types of networks: 1-hop, 2-hop, and multi-hop networks. A mathematical model for the communication channel, energy efficiency, and energy consumption is developed for all three types of networks. The proposed packet length optimization model is simulated in MATLAB software and compared with conventional bio-inspired algorithms. The results show that the proposed algorithm is very fast in finding the optimal results and transmits optimal packet size with low error rate of 0.2 p.u. and a high efficiency of 98%.     

SecDL: QoS-Aware Secure Deep Learning Approach for Dynamic Cluster-Based Routing in WSN Assisted IoT

In WSN-assisted IoT, energy efficiency and security which play pivotal role in Quality of Service (QoS) are still challenging due to its open and resource constrained nature. Although many research works have been held on WSN-IoT, none of them is able to provide high-level security with energy efficiency. This paper resolves this problem by designing a novel Secure Deep Learning (SecDL) approach for dynamic cluster-based WSN-IoT networks. To improve energy efficiency, the network is designed to be Bi-Concentric Hexagons along with Mobile Sink technology. Dynamic clusters are formed within Bi-Hex network and optimal cluster heads are selected by Quality Prediction Phenomenon (QP²) that ensure QoS and also energy efficiency. Data aggregation is enabled in each cluster and handled with a Two-way Data Elimination then Reduction scheme. A new One Time-PRESENT (OT-PRESENT) cryptography algorithm is designed to achieve high-level security for aggregated data. Then, the ciphertext is transmitted to mobile sink through optimal route to ensure high-level QoS. For optimal route selection, a novel Crossover based Fitted Deep Neural Network (Co-FitDNN) is presented. This work also concentrates on IoT-user security since the sensory data can be accessed by IoT users. This work utilizes the concept of data mining to authenticate the IoT users. All IoT users are authenticated by Apriori based Robust Multi-factor Validation algorithm which maps the ideal authentication feature set for each user. In this way, the proposed SecDL approach achieves security, QoS and energy efficiency. Finally, the network is modeled in ns-3.26 and the results show betterment in network lifetime, throughput, packet delivery ratio, delay and encryption time.

     

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.     

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.     

E2IA-HWSN: Energy Efficient Dual Intelligent Agents based Data Gathering and Emergency Event Delivery in Heterogeneous WSN Enabled IoT

Heterogeneous sensors are equipped with a limited battery source that is concerned with network lifetime problems. However, this problem can be tackled with the effective design of WSN-IoT by clustering and sleep scheduling mechanisms. This paper addresses this issue by presenting novel ideas involved in the WSN operations such as grid construction, cluster head selection, sleep scheduling, and data gathering by intelligent Agents (iAgents). An energy-efficient dual iAgents based Heterogeneous WSN (E2IA-HWSN) is proposed. iAgents are used in this paper to automatically collect the sensed data from IoT sensors. In this E2IA-HWSN, a 3?×?3 grid is built and each cell is sub-divided into four in which cluster heads (CH) are selected in each sub-division, followed by ring partitioning for selecting a CH present at the center. Multi-Objective Harris Hawks optimization (MO-HHO) algorithm is used to select CH and supernode, here to minimize the energy consumption of CH, the supernode takes responsibility to assign sleep schedules to devices. The scheduling slots are assigned only after a sensor reaches below the energy threshold. For scheduling, the Bayes rule-based Markov model (BR-MM) is applied with the determination of residual energy and sensed packet counts. Generator de Bits Pseudo Aleatorios (GBPA) eliminates redundant data in CH and then inter-cluster routing is performed in case of emergency events. If not, then the CH waits for the arrival of iagents, the trajectory of iAgents is dynamically predicted with Deep Policy Gradient (DDPG). The implementation is carried out in NS3.26 and the results show betterment to the well-known methods.

     

EHCR-FCM: Energy Efficient Hierarchical Clustering and Routing using Fuzzy C-Means for Wireless Sensor Networks

Wireless Sensor Network (WSN) is a part of Internet of Things (IoT), and has been used for sensing and collecting the important information from the surrounding environment. Energy consumption in this process is the most important issue, which primarily depends on the clustering technique and packet routing strategy. In this paper, we propose an Energy efficient Hierarchical Clustering and Routing using Fuzzy C-Means (EHCR-FCM) which works on three-layer structure, and depends upon the centroid of the clusters and grids, relative Euclidean distances and residual energy of the nodes. This technique is useful for the optimal usage of energy by employing grid and cluster formation in a dynamic manner and energy-efficient routing. The fitness value of the nodes have been used in this proposed work to decide that whether it may work as the Grid Head (GH) or Cluster Head (CH). The packet routing strategy of all the GHs depend upon the relative Euclidean distances among them, and also on their residual energy. In addition to this, we have also performed the energy consumption analysis, and found that our proposed approach is more energy efficient, better in terms of the number of cluster formation, network lifetime, and it also provides better coverage.

     

Squirrel Search Algorithm Based Support Vector Machine for Congestion Control in WSN-IoT

Wireless Personal Communications - As the Wireless sensor network (WSN) has huge part in Internet of Things (IoT), it is used in different applications, for example, detecting climate and sending...     

A QoS aware optimal node deployment in wireless sensor network using Grey wolf optimization approach for IoT applications

The growth of Wireless Sensor Networks (WSN) becomes the backbone of all smart IoT applications. Deploying reliable WSNs is particularly significant for critical Internet of Things (IoT) applications, such as health monitoring, industrial and military applications. In such applications, the WSN’s inability to perform its necessary tasks and degrading QoS can have profound consequences and can not be tolerated. Thus, deploying reliable WSNs to achieve better Quality of Service (QoS) support is a relatively new topic gaining more interest. Consequently, deploying a large number of nodes while simultaneously optimizing various measures is regarded as an NP-hard problem. In this paper, a Grey wolf-based optimization technique is used for node deployment that guarantees a given set of QoS metrics, namely maximizing coverage, connectivity and minimizing the overall cost of the network. The aim is to find the optimum number of appropriate positions for sensor nodes deployment under various p-coverage and q-connectivity configurations. The proposed approach offers an efficient wolf representation scheme and formulates a novel multi-objective fitness function. A rigorous simulation and statistical analysis are performed to prove the proposed scheme’s efficiency. Also, a comparative analysis is being carried with existing state-of-the-art algorithms, namely PSO, GA, and Greedy approach, and the efficiency of the proposed method improved by more than 11%, 14%, and 20%, respectively, in selecting appropriate positions with desired coverage and connectivity.

     

物联网核心技术与应用场景

首先对物联网的概念、组成部分以及服务体系进行简要介绍,使读者首先对物联网在整体上有较为清晰的认识,其次,依托对传感器无线射频识别技术（RFID）两种主要数据采集手段的坚实论述,融合ZigBee、WIA-PA两种常用标准,着重阐述物联网的核心-传感器网络,为读者详细论述了传感器网络中的数据采集和处理以及无线通信这两个方面。最后,展望物联网的发展前景,剖析过程中的机遇与挑战。     

Congestion control in wireless sensor and 6LoWPAN networks: toward the Internet of Things

The Internet of Things (IoT) is the next big challenge for the research community where the IPv6 over low power wireless personal area network (6LoWPAN) protocol stack is a key part of the IoT. Recently, the IETF ROLL and 6LoWPAN working groups have developed new IP based protocols for 6LoWPAN networks to alleviate the challenges of connecting low memory, limited processing capability, and constrained power supply sensor nodes to the Internet. In 6LoWPAN networks, heavy network traffic causes congestion which significantly degrades network performance and impacts on quality of service aspects such as throughput, latency, energy consumption, reliability, and packet delivery. In this paper, we overview the protocol stack of 6LoWPAN networks and summarize a set of its protocols and standards. Also, we review and compare a number of popular congestion control mechanisms in wireless sensor networks (WSNs) and classify them into traffic control, resource control, and hybrid algorithms based on the congestion control strategy used. We present a comparative review of all existing congestion control approaches in 6LoWPAN networks. This paper highlights and discusses the differences between congestion control mechanisms for WSNs and 6LoWPAN networks as well as explaining the suitability and validity of WSN congestion control schemes for 6LoWPAN networks. Finally, this paper gives some potential directions for designing a novel congestion control protocol, which supports the IoT application requirements, in future work.

     

Wormhole Attack Detection System for IoT Network: A Hybrid Approach

Many errors in data communication cause security attacks in Internet of Things (IoT). Routing errors at network layer are prominent errors in IoT which degrade the quality of data communication. Many attacks like sinkhole attack, blackhole attack, selective forwarding attack and wormhole attack enter the network through the network layer of the IoT. This paper has an emphasis on the detection of a wormhole attack because it is one of the most uncompromising attacks at the network layer of IoT protocol stack. The wormhole attack is the most disruptive attack out of all the other attacks mentioned above. The wormhole attack inserts information on incorrect routes in the network; it also alters the network information by causing a failure of location-dependent protocols thus defeating the purpose of routing algorithms. This paper covers the design and implementation of an innovative intrusion detection system for the IoT that detects a wormhole attack and the attacker nodes. The presence of a wormhole attack is identified using location information of any node and its neighbor with the help of Received Signal Strength Indicator (RSSI) values and the hop-count. The proposed system is energy efficient hence it is beneficial for a resource-constrained environment of IoT. It also provides precise true-positive (TPR) and false-positive detection rate (FPR).

     

Optimal deployment of cloudlets based on cost and latency in Internet of Things networks

With the development of the Internet of Things (IoT), a large amount of data is generated on the network edge. Given the limited computing power of mobile devices (MDs) and access to computing resources from remote clouds, which leads to high latency to MDs, edge computing provides a way to reduce service latency by building a miniature cloud (Cloudlet). MDs transfer tasks they generate to nearby cloudlets for lower latency. Although a lot of research has been done in the field of edge computing, little attention has been paid to how to deploy cloudlets in the network. In this paper, we study the cloudlet deployment on a large number of wireless access points (APs) in an IoT network to optimize both deployment cost and network latency. When the cloudlets has been deployed in the network, we propose a fault-tolerant cloudlet deployment scheme. When the original cloudlets in the network fail, the software-defined network technology is used to start the fault-tolerant cloudlets in time to ensure the stability of the network latency. To address the above problems, we propose a binary-based differential evolution cuckoo search (BDECS) algorithm, which selects the permanent cloudlet deployment location among a large number of APs on the network. Extensive simulations reveal that the proposed algorithm has better performance in minimizing cost and latency compared with other deploymegt algorithms. Moreover, the convergence speed of the BDECS algorithm is also superior to other algorithms.

     

Machine learning-based clustering protocols for Internet of Things networks: An overview

The Internet of Things (IoT) continues to expand the current Internet, opening the door to a wide range of novel applications. The increasing volume of the IoT requires effective strategies to overcome its challenges. Machine Learning (ML) has led to a growing technology that enables computers to solve problems without the need for knowledge of their intricate details. Over the past years, various ML techniques have been used to efficiently manage IoT networks. Clustering is a technique that has proven its performance in the networking domain. Many works in the literature have studied ML-based clustering methods for IoT networks, including their main properties, characteristics, underlying technologies, and open issues. In this paper, we focus on topology-centered ML-based clustering protocols for IoT networks. Specifically, we investigate the potential benefits of adopting the clustering approach to address several IoT challenges. Moreover, we provide a comprehensive taxonomy of ML-based clustering algorithms for IoT networks. Finally, we statistically analyze the incorporation of ML techniques for clustering in various IoT systems and highlight the related open issues.     

Load Balancing in Software-Defined Networks Using Spider Monkey Optimization Algorithm for the Internet of Things

In the Internet of Things (IoT), the number of devices connected to the internet, and they can collect and exchange information at any time. IoT is helpful for the progress of a smart city and different applications. Software-Defined Network (SDN) offers programmability and flexibility in the IoT network. Nevertheless, the adoption of the number of gadgets will increase the transmission delay and this will lead the network to heavy loaded. To overcome this issue, an efficient load balancing technique has to be presented in the SDN network. By considering this solution as an aim, spider monkey optimization algorithm based load balancing (LB-SMOA) is presented in this paper. Using this technique, the controller with minimum load is selected and this selected controller balances the load of the heavily loaded controller. Simulation results show that the performance of the proposed LB-SMOA outperforms the existing load balancing techniques in terms of average response time, packet loss rate, and throughput.