Cluster-based Improvised Time Synchronization Algorithm for Multihop IoT Networks

Wireless sensor networks (WSN) have specific constraints and stringent requirements in contrast to traditional wired and wireless computer networks. Among these specific requirements, energy-efficiency is the most prominent. Sensor motes have finite amount of energy supply. In most WSN applications, hundreds or thousands of motes are deployed in an area (e.g., behind enemy lines or foliage) and there is no provision for battery recharging. Therefore, a fundamental constraint on WSN applications and services is to extend the network's lifetime by making the batteries run longer. In this paper we propose an energy-efficient time synchronization protocol, referred as ETSP, which gives significant improvement in energy consumption over existing time synchronization schemes. We first evaluate the existing reference broadcast synchronization (RBS) and time-sync protocol for sensor networks (TPSN), which are two prominent examples of receiver-receiver and sender-receiver synchronization methods. This performance analysis demonstrates that combination of RBS and TPSN can work better than using these techniques separately. In the proposed ETSP protocol, we identify conditions to toggle between RBS and TPSN based on a threshold value. We experimentally and mathematically derive an optimal value of the switching threshold for different sensor motes. In this way we effectively reduce the number of transmissions which results in saving energy. Moreover, we assess the performance of our protocol on the bit error traces collected over a real WSN test bed to cater for retransmissions overload for the time synchronization protocols under consideration.

In order to avoid clock skew in WSN (Wireless Sensor Networks) for large-scale photovoltaic modules monitoring, an improved time synchronization algorithm, TSP-GDM (Time Synchronization Protocol with Gaussian Delay Model) is proposed in this paper. Interdependence of local time stamps is established between network nodes according to a linear clock model. Local exchange and share of local time stamps in nodes are achieved by means of wireless transmission. An estimation method with the Gaussian Delay Model is designed to deal with the estimation problems of the node clock offset. The synchronization accuracy of the proposed method is verified with the MATLAB simulation. It is found that TSP-GDM can be applied to synchronous topology of large-scale photovoltaic modules monitoring with a higher synchronization accuracy. Compared to RBS (Reference Broadcast Synchronization), TPSN (Timing-synchronization Protocol for Sensor Networks) and RTSP (Recursive Time-Sync Protocol for WSN), its synchronization accuracy in an inner layer has been increased by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$22.57~\mu \text{s}$ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$15.7~\mu \text{s}$ </tex-math></inline-formula> and 4.26 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{s}$ </tex-math></inline-formula> respectively.

Low-power and Lossy Networks (LLNs) have been a key component in the Internet of Things (IoT) paradigm. Recently, a standardized algorithm, namely Trickle algorithm, is adopted for routing information maintenance in such networks. This algorithm is originally designed for disseminating code updates through a wireless sensor network. Thus, when it comes to routing maintenance in LLNs, Trickle suffers from some issues related to power, convergence time, network overhead and load-distribution. In this paper, a new algorithm for maintaining the network topology in LLNs is developed motivated by Trickle weaknesses, namely, Drizzle algorithm. Unlike Trickle, Drizzle uses an adaptive suppression mechanism that permits the nodes to have different transmission probabilities consistent with their transmission history. Another distinctive feature of Drizzle in comparison with Trickle, is the absence of the listen-only period from Drizzle's intervals, thus, leading to faster convergence time. Furthermore, a new policy for setting the redundancy coefficient has been used to mitigate the negative effect of the short-listen problem presented when removing the listen-only period and to further boost the fairness in the network. Our extensive simulation experiments confirm the superiority of the proposed algorithm over Trickle under different operating conditions.

Clock synchronization is a crucial issue for data fusion, localization, duty cycle scheduling, and topology management in wireless sensor networks (WSNs). In this paper we proposed a cognitive global clock synchronization protocol (CGCSP) that is an accurate, energy efficient and reliable clock synchronization protocol in WSNs based on a single reference node. The CGCSP tackles the disadvantages of being single reference node by a cognitive switchover mechanism. This structure has been validated through the development of basic synchronization schemes i.e. sender-receiver (S-R) synchronization and receiver-receiver (R-R) synchronization. By evaluating and comparing the performances of it with state of the art protocols such as reference broadcast synchronization (RBS) and timing-sync protocol for sensor networks (TPSN), the proposed CGCSP shows reasonable lead over them in single hop network as well as in multi hop networks in terms of average synchronization accuracy and energy efficiency.

Internet of Things (IoT) networks are being designed to connect billions of sensors/devices for an application-specific environment to provide services and carry out special tasks. It enables the virtual connection of the real-world physical devices with Internet cyberspace. While, there is an exponentially growing trend in the use of IoT technology for diverse applications such as smart agriculture, smart industries, smart hospitals, smart homes, etc., there are pressing design issues which affect the IoT network performance in the long run, for example, replacement of batteries for increasing the lifetime of devices to maintain the network longevity. To tackle this challenge, various wireless powered-based energy transfer paradigms were proposed over time. Backscatter communication (BackCom) is likewise one of the energy-efficient solutions proposed, which can satisfy the stringent green communication aspects of IoT networks. To achieve massive connectivity in BackCom-enabled IoT network, selecting a suitable multiple access scheme is also a crucial part. Most multiple access schemes proposed for BackCom systems are based on frequency or timeslots sharing techniques. Multiple access techniques proposed for BackCom IoT system can reap the gain of non-orthogonal multiple access (NOMA) principle. In NOMA, multiple end nodes (ENs) of sensors network is served in the same resource block, thereby, increasing the spectrum efficiency of BackCom IoT system. In this chapter, we discuss the prospects of backscatter technique with application of NOMA for green communication in IoT network.

Wireless sensor networks (WSNs) of spatially distributed autonomous sensors are used to monitor physical or environmental conditions such as temperature, sound, pressure, etc. They are also used to cooperatively pass the collected data through the network to a main location. Due to the application of wireless sensor networks as a monitoring device in the real world, the physical time of the occurrence of events is important. Since WSNs have particular constraints and limitations, synchronizing the physical times for these networks is considered to be a complex task. Although many algorithms have been proposed for synchronizing time in the network, there are two main error factors in all the proposed algorithms. The first factor is the clock drift which might be caused by the influence of different environmental factors such as temperature, ambient temperature, humidity, it might be generated on crystal oscillator which is inevitable The second error factor is indeterminacy which is attributed to the existence of non-deterministic delays in sending and receiving messages between sensor nodes. These two factors together reduce the precision of synchronization algorithms. In this paper, the researchers proposed a new approach for dealing with the above-mentioned two problems and achieving better synchronization. The proposed approach is a combination of flooding time synchronization protocol (FTSP) and reference broadcast synchronization (RBS).This approach is intended to increase synchronization accuracy and network lifetime by reducing the number of synchronization messages sent between nodes and eliminating the most of non-deterministic errors in sending messages. The results of simulations conducted in the study indicated that the proposed approach is significantly more efficient than the FTSP and RBS methods in terms of parameters such as accurate synchronization, amount of sent packets and power consumption.

Wireless communication networks are highly prone to security threats. The major applications of wireless communication networks are in military, business, healthcare, retail, and transportations. These systems use wired, cellular, or adhoc networks. Wireless sensor networks, actuator networks, and vehicular networks have received a great attention in society and industry. In recent years, the Internet of Things (IoT) has received considerable research attention. The IoT is considered as future of the internet. In future, IoT will play a vital role and will change our living styles, standards, as well as business models. The usage of IoT in different applications is expected to rise rapidly in the coming years. The IoT allows billions of devices, peoples, and services to connect with others and exchange information. Due to the increased usage of IoT devices, the IoT networks are prone to various security attacks. The deployment of efficient security and privacy protocols in IoT networks is extremely needed to ensure confidentiality, authentication, access control, and integrity, among others. In this paper, an extensive comprehensive study on security and privacy issues in IoT networks is provided.

The effect of time synchronization within the sensor nodes in Wireless Sensor Networks is an ongoing research area. It has received a huge amount of attention in the last decade. This paper discusses nature inspired algorithm, Ant Colony Optimization based Time-Sync(ACOTS) algorithm to provide an energy optimized solution to resolve time synchronization in WSN. Ant Colony optimization algorithm is implemented, and it was observed that it drastically reduces the number of packets transmitted and received during the synchronization process. This would help to solve time synchronization problem as well as it will also create an energy efficient network. The proposed algorithm ACOTS is compared with the already existing algorithms for e.g. Reference-Broadcast Synchronization (RBS) and Time-Sync Protocol for Sensor Network (TPSN). Comparative results suggested ACOTS uses less exchange of messages than other schemes.

The environmental impacts that are associated with the interconnected sensor devices in the Internet of Things (IoT) network are minimized with the utilization of green communication. This is because, when the IoT devices tend to grow, the IoT system becomes more prone to issues regarding the utilization of resources, sustainability, and energy consumption within the communication system. Tuning the communication protocols, lowering the IoT device’s energy usage rate, and establishing an effective technique for the transmission of data are performed. This is achieved by the green communication technique, which aids in the establishment of significant energy savings in the IoT network. For devices with limited power sources, the green communication technology offers an extended battery life. Despite the enormous potential of IoT technology, a number of obstacles need to be overcome, including those related to load balancing, security, storage, privacy, energy management, and device heterogeneity. In response to the challenges posed by conventional models, an innovative solution for the selection of Cluster Head (CH) using a hybrid approach is devised here. The energy consumption of sensor nodes is influenced by various factors, such asCH load, temperature, distance, residual energy, the number of alive nodes, delay, and so on. To address these challenges in IoT networks, a hybrid approach that combines the Squid Game Optimizer (SGO) and the Artificial Gorilla Troops Optimizer (AGTO) for the optimal selection of CH is suggested. The implemented approach is named Hybrid Squid Game with Artificial Gorilla Troops Optimizer (HSG-AGTO). This approach aims to optimize the above-mentioned factors and overcome energy consumption challenges in IoT networks. The effectiveness of the model is validated, and the results demonstrate the superior performance of the suggested model.

Time synchronization is a significant component in Wireless Sensor Networks (WSNs) for maintaining synchrony among nodes. Most time synchronization protocols in WSNs utilize dedicated synchronization packets for clock accuracy optimization. However, in practical WSNs, the joint design of time synchronization and Media Access Control layer protocols should be considered. One approach is adding timestamps directly into data packets and the corresponding acknowledgements (ACKs). Therefore, communication and energy overhead could be saved greatly and time synchronization could be seamlessly integrated into networks. In this letter, we investigate the time synchronization scheme of listening nodes overhearing the neighboring two-way timing packet exchange based on periodical ACK mechanism and present an efficient clock skew estimation algorithm with clock correction upon every synchronization. The Maximum Likelihood Estimator (MLE) of clock skew for Gaussian random packet delay is derived, and the corresponding Cramer-Rao Lower Bound (CRLB) is obtained. In addition, the MLE shows that the clock skew could be estimated without any prior knowledge of the fixed packet delay and the time of adjustment. Simulation results verify that the MLE is efficient.

Wireless Sensor Networks (WSNs) are a major element of Internet of Things (IoT) networks which offer seamless sensing and wireless connectivity. Disaster management in smart cities can be considered as a safety critical application. Therefore, it becomes essential in ensuring network accessibility by improving the lifetime of IoT assisted WSN. Clustering and multihop routing are considered beneficial solutions to accomplish energy efficiency in IoT networks. This article designs an IoT enabled energy aware metaheuristic clustering with routing protocol for real time disaster management (EAMCR-RTDM). The proposed EAMCR-RTDM technique mainly intends to manage the energy utilization of nodes with the consideration of the features of the disaster region. To achieve this, EAMCR-RTDM technique primarily designs a yellow saddle goatfish based clustering (YSGF-C) technique to elect cluster heads (CHs) and organize clusters. In addition, enhanced cockroach swarm optimization (ECSO) based multihop routing (ECSO-MHR) approach was derived for optimal route selection. The YSGF-C and ECSO-MHR techniques compute fitness functions using different input variables for achieving improved energy efficiency and network lifetime. The design of YSGF-C and ECSO-MHR techniques for disaster management in IoT networks shows the novelty of the work. For examining the improved outcomes of the EAMCR-RTDM system, a wide range of simulations were performed and the extensive results are assessed in terms of different measures. The comparative outcomes highlighted the enhanced outcomes of the EAMCR-RTDM algorithm over the existing approaches.

Source-location privacy (SLP) protection improves security in event monitoring wireless sensor networks (WSNs) for Internet of Things (IoT) applications. However, due to constrained energy resources, WSNs incur coverage holes that affect the level of SLP protection. Existing studies have ineffectively analysed the effects of coverage holes on SLP protection. Noting the limitation, this study investigated the coverage hole effects. Performance of various SLP routing protocols was evaluated based on the sensor node utilization ratio, attack success rate, end-to-end delay, and packet delivery ratio. Then, considering the challenge of limited battery power in IoT sensors, the performance gains and limitations of the protocols were identified. Simulation results demonstrate that the data dissemination SLP routing protocol (DIRP) outperforms other protocols. However, the performance of DIRP is significantly affected by coverage holes. The results suggest that for DIRP to be viable in IoT applications, the integration of distributed energy resources should be considered.

New technologies and communication standards have changed traditional network processes. Internet of Things (IoT) is one of the emerging technologies where devices are connected to facilitate the users. When the networks are more congested due to a large number of users then the existing routing protocol and communication channels suffer from congestion, disconnection, overhead, and packet drop issues. Unmanned Aerial Vehicles (UAVs) are adopted to support the ground networks for more feasible data communication. These networks provide coverage facilities to IoT networks and provide smooth data dissemination services. Through the use of relay and cooperative communication technologies, UAVs could enlarge the communication space for IoT networks. Traditional network routing protocols have been adopted for data communication in these networks. However, the adopted protocols are not able to handle mobility and uncertain network conditions. This paper proposes a Decision-based Routing for Unmanned Aerial Vehicles and Internet of Things (DR-UAVIoT) network. The proposed protocol is useful for UAV-to-IoT and UAV-to-UAV data communication. The performance of the proposed solution is evaluated with the existing protocols in terms of data delivery, delay, and network overhead. The experimental results indicate the better performance of the proposed protocol in terms of less delay, less overhead, and better data delivery ratio as compared with existing routing protocols.

A blockchain-enabled Model integrates blockchain technology with Intrusion Detection Systems to enhance the security of Internet of Things (IoT) networks. It ensures data integrity, decentralization, and tamper-proof logging of intrusion detection. The approach improves trust, transparency, and real-time threat detection in distributed IoT environments. The existing blockchain-based IDS approaches, Blockchain Enabled (BCE-IoT), uniquely integrate blockchain consensus with federated-style local training, lightweight cryptography, and Shapley Additive Explanations (SHAP)-based explainability, ensuring both security and interpretability in IoT environments. The proposed work combines Blockchain technology with explainable artificial intelligence solutions to create a new cybersecurity Model that strengthens intrusion detection within IoT networks. The proposed model enhances transparency in tracking cyberattacks by combining blockchain security storage capabilities with SHAP, an explainable AI. This research utilises machine learning and artificial intelligence to detect threats in real-time, countering Distributed Denial of Service (DDoS), Denial of Service (DoS), scanning, Cross-Site Scripting (XSS), injection, password, and backdoor attacks. BCE-IoT delivers more precise security by combining blockchain’s permanent data features and AI anomaly detectors, thereby reducing security alert mistakes. The performance effectiveness of Blockchain-Enabled IoT surpasses that of the Content Integrity Detection System. It combines Blockchain and Software-Defined Networking to enhance security in network environments, utilising blockchain-based mutual confirmation for software-defined networking to detect and block cyber threats. The evaluation establishes BCE-IoT as an effective IoT network security solution that delivers strong cybersecurity features, is adaptable to modern connected environments, and offers interpretable security solutions. The performance evaluations demonstrate that BCE-IoT provides a robust, flexible, and interpretable cybersecurity solution suitable for modern IoT environments.

A fuzzy based Co-Operative Blackmailing Attack detection scheme for Edge Computing nodes in MANET-IOT environment