Hybrid Evolutionary Algorithm with Energy Efficient Cluster Head to Improve Performance Metrics on the IoT

In recent years, a new wave of networks labelled Wireless Sensor Networks (WSNs) has attracted a lot of attention from researchers in both academic and industrial communities. A WSN consists of a collection of sensor nodes and a base station connected through wireless channels, and can be used for many applications such as military application, building distributed systems, physical environment monitoring, and security surveillance among others. A big advantage of sensor networks is represented by ease of deployment, reducing installation cost, possibility to distribute the tiny sensors over a wide region, and larger fault tolerance (V. Loscri et al., 2005). However, despite the infinite scopes of wireless sensor networks applications, they are limited by the node battery lifetime. Such constraints combined with a typical deployment of large number of sensor nodes have posed many challenges to the design and management of sensor networks and necessitate energyawareness at all layers of the networking protocol stack (Q. Xue & A. Ganz, 2004). Therefore, energy efficient algorithms have been one of the most challenging issues for WSNs. Sensor nodes can be in one of four states, namely transmit, receive, idle and sleep. The largest part of a node’s energy is consumed while transmitting and receiving. Minimizing the number of communications by eliminating or aggregating redundant sensed data saves much amount of energy (L. B. Ruiz et al., 2003). Among these clustering sensor networks are a very attractive approach because clustering allows for scalability, data aggregation, and energy efficiency. In a clustering network, nodes are grouped into clusters and there are special nodes called cluster head. They are responsible for an efficient way to lower energy consumption within a cluster by performing data aggregation. In a heterogeneous sensor network, two or more different types of nodes with different battery energy and functionality are used. On the other hand, in homogeneous networks all the sensor nodes are identical in terms of battery energy and hardware complexity. As a result, network performance decreases since the cluster head nodes goes down before other nodes do. Thus dynamic, energy efficient and adaptive cluster head selection algorithm is very important. Sensor networks can be divided in two classes as event-driven and continuous dissemination networks according to the periodicity of communication (L. B. Ruiz et al., 9

여러 사물 인터넷 기기들을 조합, 활용하여 다양한 서비스를 제공할 수 있게 된 환경에서 추천시스템은 사용자가 원하는 서비스를 선택하는 데 있어 도움을 줄 수 있다. 기존의 추천 시스템에 대한 많은 연구는 주로 개인 사용자 대상의 추천에 집중되어 있으나 사물 인터넷 환경에서는 개인과 그룹 모두 사용자가 될 수 있으므로 그룹에 대한 추천 방법이 필요로 하다. 본 연구는 사물 인터넷 환경에서 그룹사용자들의 서비스 선호도를 분석하고, 이를 바탕으로 특정 장소에서 서비스를 이용한 적이 없는 새로운 그룹 사용자에게 서비스를 추천할 수 있는 방법을 개발하였다. 본 연구에서는 그룹의 구성 정보를 기반으로 그룹들 간의 유사도를 측정하여 사용자 기반 협업 필터링을 적용하였다. 실험에서는 실제 사물 인터넷 테스트 베드 환경에서 수집된 데이터를 사용하였으며 실험 결과를 통해 제안한 서비스 추천 방법이 효과적임을 확인할 수 있었다.

Recently, IETF (Internet Engineering Task Force) presented CoAP (Constrained Application Protocol) for the communication between sensor and actuator nodes in a constrained environment such as small amount of memory and low power. IETF CoAP protocol can convert easily, and can use to monitor or control infrastructure utilities through low-power sensor and actuator networks in IoT (Internet of Things) and M2M (Machine-to-Machine) environment. IETF CoAP protocol provides the special requirements of this constrained environment, especially considering energy, automation, and other IoT applications. In this paper, we design and implement a node registration and discovering based CoAP in IoT environment. The node is a CoAP node which is designed for working in constrained environment. For node registration in IoT, we used CoRE RD (Resource Directory) functionalities to register CoAP node’s information which works in IoT environment.

Designed as a specialized ad hoc network suitable for applications such as emergency responses, OppNets are considered as a sub-class of DTN where communication opportunities are intermittent, so an end-to-end path between the source and the destination may never exist. Existing networks have already brought connectivity to a broad range of devices, such as hand held devices, laptops, tablets, PC, etc. The Internet of Things (IoT) will extend the connectivity to devices beyond just mobile phones and laptops, but to buildings, wearable devices, cars, different things and objects. There are different issues for these networks. One of them is the selection of IoT nodes in order to carry out a task in opportunistic networks. In this work, we implement a Fuzzy-Based System for IoT node selection in opportunistic networks. For our proposed system, we use four input parameters: IoT Node’s Unique Encounters (NUE), IoT Node’s Free Buffer Space (NFBS), IoT Node’s Batter Level (NBL) and IoT Node Contact Duration (NCD). The output parameter is IoT Node Selection Decision (NSD). The results show that the proposed system makes a proper selection decision of IoT nodes in opportunistic networks. The IoT node selection is increased up to 25% and 11% by increasing NCD and NUE, respectively.

The rapid growth in wireless technology is enabling a variety of advances in wireless sensor networks (WSNs). By providing the sensing capabilities and efficient wireless communication, WSNs are becoming important factor in day to day life. WSNs have many commercial, industrial and telecommunication applications. Maximizing network lifespan is a primary objective in wireless sensor networks as the sensor nodes are powered by a non-rechargeable battery. The main challenges in wireless sensor networks (WSNs) are area of coverage, network’s lifetime and aggregating. Balanced node establishment (clustering) is the foremost strategy for extending the entire network's lifetime by aggregating the sensed information at the head of the cluster. The recent research trend suggests Meta-heuristic algorithms for the intelligent selection of ideal Cluster Heads (CHs). The existing Cluster Head Selection (CHS) algorithm suffers from the inconsistent trade-offs between exploration – exploitation and global best examine constraints. In this research, a novel Camel series Elephant Herding Optimization (CSEHO) algorithm is proposed to enhance the random occurrences of Camel algorithm by the Elephant Herding Optimization algorithm for optimal CHS. The Camel algorithm imitates the itinerant actions of a camel in the desert for the scavenging procedure. The visibility monitoring condition of the camel algorithm improves the efficiency of exploitation, whereas the exploration inefficiency of a Camel algorithm is compensated optimally by the Elephant Herding Optimization operator (Clan and separator). The superior performance of the proposed CSEHO algorithm is validated by comparing its performance with various other existing CHS algorithms. The overall attainment of the offered CSEHO algorithm is 21.01%, 31.21%, 44.08%, 67.51%, and 85.66%, better than that of EHO, CA, PSO, LEACH, and DT, respectively.

Secure three party node authentication and key establishment scheme for data exchange in the Internet of Things (IoT) applications enables two resourceconstrained nodes to establish a secure end-to-end communication channel with the help of a data server. Since node in IoT have constraints on resources such as power, memory space and computation ability. Thus may existing key establishment schemes are unable to run IoT applications and many researchers are already working on how to integrate new techniques and efficient approaches into the IoT environment. Recently, Nasiraee and Mohasefi proposed a highly efficient and novel key establishment scheme for Internet-Enable Sensor Networks (IESN) which was adapted to the IoT notion. Nasiraee-Mohasefi’s scheme presented a novel approach where a new node that joins the IoT network is responsible to aggregate interested neighbors’ information and to send a request to the trusted server to get required pairwise session keys. However, we found that Nasiraee- Mohasefi’s scheme has some security and efficiency shortcomings and this paper focuses on preventing the above-mentioned weaknesses of Nasiraee-Mohasefi’s scheme by proposing an improved three party node authentication and key establishment scheme. The results of security proof by BAN logic analysis confirms the proposed scheme provides a considerable gains in power saving while its security properties are ensured for the Internet of Things environment.

Internet of Things-based environments pose various challenges due to their anytime/anywhere computing, and the efficient cryptographic based key management is one of the major challenges in Internet of Things. The key management life cycle consists of initialization, key generation, key registration, key backup, key update, key recovery, and key revocation. Our contribution in this paper is to summarize the state-of-the-art key management schemes and techniques in different scenarios, such as mobile ad hoc networks, wireless sensor networks, and the Internet of Things environments. Further different issues related specifically to the Internet of Things environment are discussed and the causes and effects pertaining to the security breach for Internet of Things are identified. Furthermore, in this research work, we develop a novel permutation of threshold and identity-based key management schemes for the Internet of Things environment and have proposed future directions to counteract the attacks on confidentiality, integrity, authentication, and availability of security services in the Internet of Things environment and identified the two key management schemes, that is, identity and threshold schemes for Internet of Things, to resolve Internet of Things key management issues and maximum possible security services effective implementation. We evaluate our scheme with the help of mathematical and statistical techniques.

Maintaining adequate energy in low-powered Internet of Things (IoT) nodes is crucial for the development of several applications like smart homes, autonomous industries, etc. These IoT nodes exploit adaptive duty cycling techniques for the efficient utilization of energy resources. However, such adaptive duty cycling of IoT nodes results in their asynchronous operations thereby inducing energy holes in the network. These energy holes lead to information loss and poor quality of services of IoT networks. In this regard, energy harvesting using Mobile Energy Transmitters (MET) can improve the lifetime of an IoT network. In this work, we are introducing a metric named Age of Charging (AoC) metric to quantify the repetitive charging of power deficit IoT nodes. Energy-efficient scheduling of MET is proposed to minimize the expected average AoC such that the energy harvested by IoT nodes is maximized. In this regard, the optimization problem is first remodeled into a Markov decision process. Subsequently, a deep reinforcement learning algorithm is developed based upon the twin delayed deep deterministic policy gradient scheme for energy-efficient scheduling of MET in asynchronous IoT networks. The simulation results indicate that the proposed algorithm outperforms the conventional Deep Q-networks and soft-actor-critic algorithms. These results motivate the usage of MET-aided energy harvesting in self-sustaining IoT networks.

The energy efficiency paradigm is a major bottleneck for the development of wireless sensor networks (WSNs) and Internet of Things (IoT) architectures and technologies. This edited book presents comprehensive coverage of energy harvesting sources and techniques that can be used for WSN and IoT systems

In the modern era, web data retrieval and data analytics play a crucial role for taking intelligent decisions in Internet of Things (IoT) environment. In IoT environment, various objects perform the tasks of sensing and computation for providing uninterrupted services (e.g., e-health, e- transportation, security access, etc.) to the end users. However, accessing the relevant and accurate information with reduced delay is still a challenging task in IoT environment. Although this aspect has been explored in the literature, the existing proposals have high complexity and require long time for accessing the relevant information from different IoT objects located across the globe. The information may be located across different web pages, which are linked together irrespective of their geographical locations. So, this paper addresses the issues such as accuracy, context- aware, reduced delay with low complexity in accessing the information from a remote device by the end users. In the proposed scheme, the strength of a web page which contains the relevant information to be fetched is judged by the quality of content and the inter- connections between different web pages. The proposed scheme simplifies the rank score calculation of these web pages and provides quality web pages at the top of the search result pages by demoting spam web pages. Bias connected web pages are verified by the linkage information of spam web pages. The Quality Assessment for Information Retrieval (QAIR) algorithm is proposed for the classification of web pages. The proposed algorithm computes the QAIR score by evaluating the web page quality. Microsoft Learning to Rank dataset has been used for the experiments, which consists of 239092 query-url pairs. By using this dataset, the computed QAIR score is compared with the PageRank score. This comparison determines the category of web page, i.e., either the page is strong or weak. The proposed scheme has been validated with decision tree followed by ten- fold cross validation, which results in an accuracy of 92.4%.

Current technology driven world is moving forward with massive implementation of smart objects. These smart objects or Internet of Things (IoT) nodes are involved in sensing of real time critical data from surroundings. Though these IoT nodes are involved in very specific task but their existence is very important for the complete system. Usually, in an IoT architecture, several heterogeneous nodes connect with a nearest gateway. The gateway itself connects with the world via Internet. IoT nodes need to flexible about connectivity as those may need to leave and join any gateway based on context (time, location etc.). To achieve flexibility, IoT components need a trust relationship between each other. In this paper, we have proposed a mechanism for integrating distributed trust management in IoT by using edge computing technology, considering the scalability and heterogeneous capability of IoT devices.

Energy efficiency is critical in Wireless Sensor Networks (WSNs), pivotal for Internet of Things (IoT) applications like environmental monitoring and smart cities. Clustering protocols like Low-Energy Adaptive Clustering Hierarchy (LEACH) extend network lifetime through Cluster Head (CH) rotation, but random CH selection often leads to suboptimal energy use. This study evaluates eight hybrid Particle Swarm Optimization-based LEACH (PSO-LEACH) variants to optimize CH selection and clustering: FA-PSO-LEACH (Firefly Algorithm), CSA-PSO-LEACH (Crow Search Algorithm), PSO-LEACH with CBTEERA, Adaptive PSO-LEACH, ABC-PSO-LEACH (Artificial Bee Colony), ACO-PSO-LEACH (Ant Colony Optimization), WOA-PSO-LEACH (Whale Optimization Algorithm), and Hybrid GA-PSO-LEACH (Genetic Algorithm). The primary research objective is to compare these variants in terms of energy efficiency, network lifetime, and clustering quality to identify the most effective strategies for prolonging WSN operation in IoT environments. Specifically, this paper addresses the research question: Which hybrid PSO-LEACH variant provides the optimal balance between early-stage stability and long-term energy conservation in a simulated WSN with 50 nodes? Each variant uses a unique metaheuristic for CH selection based on residual energy and distance, with PSO refining clustering. A Python simulation modelled a 100m × 100m WSN with 50 nodes, assessing network lifetime (First Node Died, FND; Half Nodes Died, HND; Last Node Died, LND), energy consumption, and clustering quality. Results show ABC-PSO-LEACH leading with FND at 57 rounds and LND at 462 rounds, followed by WOA-PSO-LEACH (HND 148, LND 406), surpassing others (e.g., PSO-LEACH with CBTEERA: FND 10, LND 280). WOA-PSO-LEACH’s balanced energy distribution excels in mid-to-late stages, despite computational overhead. These bio-inspired hybrids outperform traditional LEACH, with ABC-PSO-LEACH and WOA-PSO-LEACH ideal for prolonged WSN operation. Future work aims to enhance early-stage performance and explore dynamic topologies.

Internet of Things (IoT) is being applied to areas as smart-cities, home environment, agriculture, industry, etc. Developing, deploying and testing IoT projects require high investments on devices, fog nodes, cloud nodes, analytic nodes, hardware and software. New projects require high investments on devices, fog nodes, cloud nodes, analytic nodes, hardware and software before each system can be developed. In addition, the systems should be developed to test them, which implies time, effort and development costs. However, in order to decrease the cost associated to develop and test the system the IoT system can be simulated. Thus, simulating environments help to model the system, reasoning about it, and take advantage of the knowledge obtained to optimize it. Designing IoT simulation environments has been tackled focusing on low level aspects such as networks, motes and so on more than focusing on the high level concepts related to IoT environments. Additionally, the simulation users require high IoT knowledge and usually programming capabilities in order to implement the IoT environment simulation. The concepts to manage in an IoT simulation includes the common layers of an IoT environment including Edge, Fog and Cloud computing and heterogeneous technology. Model-driven development is an emerging software engineering area which aims to develop the software systems from domain models which capture at high level the domain concepts and relationships, generating from them the software artefacts by using code-generators. In this paper, a model-driven development approach has been developed to define, generate code and deploy IoT systems simulation. This approach makes it possible to design complex IoT simulation environments and deploy them without writing code. To do this, a domain metamodel, a graphical concrete syntax and a model to text transformation have been developed. The IoT simulation environment generated from each model includes the sensors, actuators, fog nodes, cloud nodes and analytical characteristics, which are deployed as microservices and Docker containers and where elements are connected by using publish-subscribe communication protocol. Additionally, two case studies, focused on smart building and agriculture IoT environments, are presented to show the simulation expressiveness.

We propose a framework that enables the cluster head (CH) to harvest energy from uplink transmission by Internet of Things (IoT) nodes employing data compression under nonorthogonal multiple access (NOMA) scheme. Our framework enables the CH to maximize the harvested energy while meeting constraints on outage probability, consumed energies by the transmitting IoT nodes and compression and distortion ratios. We provide necessary analysis for our framework and derive an expression for the outage probability and average harvested energy under the NOMA scheme. We formulate an optimization problem with NOMA factors, simultaneous wireless information and power transfer (SWIPT) factors, and NOMA user distances as optimization parameters. We first solve the optimization problem and find the optimized values using a grid-based search. Then, we exploit a deep reinforcement learning algorithm to solve the optimization problem more efficiently. Throughout this work, we prove the feasibility of such framework and deliver key observation that will help the CH scheduling different IoT nodes such that the harvested energy is maximized while the constraints are met.

Plant health monitoring is crucial in ensuring a constant food supply to satisfy the growing demand for food. Hence, it is essential to monitor plant health to maximize the yield and minimize the risk of various diseases. Soil moisture and temperature are of critical importance in plant growth, and predicting them enables farmers to take preventive actions, thereby mitigating the issues affecting plant health. This work presents a plant health monitoring approach by forecasting soil moisture and heat levels by collecting data in an Internet of Things (IoT) environment. Here, for transmitting the soil data acquired by the IoT nodes, a cluster head (CH) selection and routing technique using Gannet Namib beetle optimization (GNBO) is used. The data is routed to a prediction module, wherein soil moisture and heat levels are predicted by Convolutional long short term memory (Conv-LSTM). Furthermore, the hyperparameters of the Conv-LSTM are optimized by the GNBO algorithm. The efficiency of the GNBO-Conv-LSTM is examined based on link life time (LLT), energy, delay, distance, negative predictive value (NPV), positive predictive value (PPV), and true negative rate (TNR) and is observed to have achieved values of 0.675, 0.478 J, 0.092 ms, 50.200 m, 0.885, 0.882, and 0.875, correspondingly.