Abstract
The internet of things is one of the most significant and promising innovations today. In this chapter, the authors proposed the dual probability-based energy estimation model in the wireless sensor network. The dual probability-based function measures the expected value of energy for the transmission of data. This function creates a subgroup of networks based on energy function and carries out the operation of energy management in the context sensor node data processing. This function also integrates cloud-based services with the sensor networks. The benefit of this function is that it increases the throughput of network and quality of service. The proposed model was simulated in MATLAB R-2014a environment, and the results were obtained using different scenarios of network density. Finally, the authors analyzed the performance of our proposed work with respect to the following metrics: data utility, energy consumptions, and data reconstruction error.
TopIntroduction
The concept of the internet of things integrates all devices with the internet. It includes sensors, actuators, and edge devices. The life of network devices depends on the consumption and utility of energy in the sensor node or network. The wireless sensor network in cooperative technology is a challenge in IoTs. The application of IoTs is linked with the edge network (Nguyen et al., 2021). The edge network supports the concept of the dynamic nature of cloud-based services. The edge-based component has a problem with bandwidth and energy. Energy is a major factor in the sensor node for data transmission and data receiving. Due to the mobile nature of the sensors node, the consumption of energy is very high and the life of IoT devices (Ullah et al., 2021). In the current decade, the minimization of energy in a wireless sensors network is a big issue. For the minimization of energy, various low-cost based energy protocols are designed. The success story of wireless sensor network deals with the success of IoT based services over cloud environments. The minimization of energy in wireless sensor networks is possible to use various routing and MAC layer based protocols. The duty cycle based routing protocol also reduces energy consumption during the transmission of data over the sink node. Some authors also suggested the cluster-based routing protocol for the minimization of energy in wireless sensors networks (Kaur et al., 2017).
The internet of things (IoT) is a group of internet enables things. The internet of things provides services to all societal areas. The things basically deal with electronic communication objects connected through the internet. The acceptability of IoTs is increasing every day due to easy installation and low-cost maintenance. The IoTs change the scenario of remote area data accessing, for accessing the remote area data, such as temperature, pressure, weather and fire event in the forest sensor networks are used. The sensors collect the information and transmit it to the base station with IoTs devices. Nowadays the IoTs application is integrated with cloud-based services. The cloud-based services are deployed over smart devices (Huang et al., 2014). The cloud services basically support the static infrastructure. The IoTs integrate these services with dynamic infrastructure. The dynamic nature of the cloud enhances the reachability of IoTs services to the most distant the universe. Along these lines, IoTs can bring forth colossal valuable applications and administrations that we never envisioned. With the progression in innovation, the device’s processing power and storage capacity significantly expanded while their sizes diminished. These smart devices are normally outfitted with various kinds of sensors and actuators. In addition, the physical objects are progressively outfitted with RFID labels or other electronic standardized identifications that can be scanned by smart devices, e.g., smart mobile phones or small installed RFID scanner. IoTs is the Internet's stretching out and growing to the physical world and its related properties incorporate center, content, gathering, figuring, correspondences and network situations. These properties demonstrate the consistent association among individuals and objects or between the items and objects.
The current trend of research focuses on minimization of energy in terms of data transmission and data receiving in wireless sensor network for IoTs. The researchers proposed the techniques based on clustering, low route cost, heuristic based optimization to improve energy efficiency of sensor nodes. The sensor nodes usually work for long time in idle because it causes the waste of energy in WSNs. Now the concept of active and sleep mode, based on duty cycle, enhanced the efficiency of energy. The active and sleep mode increase the use of energy but degrade the performance of network in terms of quality of service, delay and loss of data packet. In this paper, dual probability based energy estimation function for the reduction of energy during the transmission of data to the sink node has been proposed (Sharma et al., 2021). The dual probability based energy function works on the concept of dynamic optimization of route cost and energy. The function of energy works on different layers of energy such as high energy, middle energy and low energy.
Key Terms in this Chapter
Service-Oriented Architectures: Service-oriented architecture (SOA) is an architectural style that defines the use of services to support a wide range of business processes and activities. It is an approach to designing software systems that focuses on creating modular and loosely-coupled services that can be used independently and can communicate with each other using standardized protocols.
Compressive Sensing: This is a signal processing technique that allows the reconstruction of a signal from only a small number of measurements or samples. It has applications in a variety of fields such as image processing, radar imaging, and medical imaging. The key idea behind compressive sensing is that a signal's sparsity or compressibility in a certain domain can be exploited such that fewer measurements are needed to accurately reconstruct the signal. This reduces data acquisition time and storage requirements, making it a useful tool in situations where data acquisition and storage resources are limited. Compressed sensing is a signal processing technique for efficiently acquiring and reconstructing a signal, by finding solutions to underdetermined linear systems. This is based on the principle that, through optimization, the sparsity of a signal can be exploited to recover it from far fewer samples than required by the Nyquist–Shannon sampling theorem.
Wireless Sensor Network: It is the network of sensors that are connected wirelessly to each other and to a central computer or server. These networks are used to gather and transmit data from various environmental or industrial sources, such as temperature and humidity sensors, traffic monitoring sensors, or security cameras.
Sensor Node: A sensor node is a small electronic device that is designed to collect and transmit data from the environment or surrounding area. It usually consists of a sensor, a microcontroller, a wireless communication module, and a power source.
RFID: RFID (Radio Frequency Identification) is a wireless technology used for identifying and tracking tagged objects. This technology is commonly used for inventory management, supply chain management, asset tracking, and access control. It is also used in contactless payment systems and in identifying pets and livestock.
Dual Probability: Dual probability sensor nodes are devices that use two different sensing mechanisms to detect the presence or absence of a particular event or condition. These sensors are frequently used in automation and control systems to detect and respond to changes in their environment or operating conditions.
Internet of Things: The Internet of Things (IoT) is a network of physical devices, vehicles, home appliances, and other items that are embedded with sensors, software, and network connectivity that enables these objects to collect and exchange data.
Energy Consumptions: A useful measure of the efficiency of the network is the energy consumed per bit of data transferred. In Wireless Sensor Networks (WSNs), energy consumption is an important consideration since the network's longevity and performance depend on it.
Inter-Cloud Service Exchange: Inter-cloud service exchange refers to a marketplace that allows cloud service providers and consumers to exchange services and data across different clouds. It allows businesses to consume and provide services across multiple cloud providers.
Local Cloud: This is a Cloud Computing model that allows companies and organizations to have their own private cloud infrastructure. This infrastructure is usually built on an on-premises server or data center, and it provides similar benefits to public cloud services, such as scalability, reliability, and cost-effectiveness.