Service-Oriented Transparent Interconnection between Data-Centric WSN and IP networks

Transmission of energy efficiently is a major goal of Wireless Sensor Network for its lifetime. Wireless sensor network consumes more power, as nodes perform on restricted power batteries which decrease its lifetime, a vast field for researchers to work in several fields like industrial automation, environmental monitoring and military applications. Implementation of error control scheme for reduce of bit error rate (BER) is essential‥ In this work, we aim to evaluate the performance analysis of BPSK and QPSK modulation with different strategies using different analysis parameters such as Bit Error Rate (BER), energy used. The ratio of BER and SNR is analyzed in this paper. This paper may be used and modified further for better analysis of different modulation schemes to improve the lifetime of the clustered sensor network.

Wireless sensor networks (WSN) have recently been proposed for a large range of applications in home and industrial automation, and for health and environment monitoring. Especially the ZigBee standard is getting rising attention as it can be used to implement wireless sensor networks, because of its low data rate and low power consumption. Increasingly there is a need to access wireless sensor network services from other IP- based networks. The DPWS has been chosen to implement the integration of the different forms of networking services. DPWS uses web services, XML, WSDL and SOAP protocol to connect various devices and services in home and industrial applications. It allows collaboration and dynamic reconfiguration of network services and devices. This paper will describe our idea of how DPWS can be used to integrate a wireless sensor network into other IP-based networks.

Recently, the demand of Wireless Sensor Network (WSN) increased rapidly due to the vast applications in environmental monitoring, health care, Smart cities, Industrial automation, agriculture and Transports, etc. However, limited battery backup in sensor nodes affects the lifetime of the WSN. In this paper, a novel multi objective optimization problem is formulated to maximize the life time by considering the distance distribution of sensor nodes with their Cluster Heads (CHs) and distribution of CH’s life time. The recent multi objective algorithms like Decomposition based Multi Objective Evolutionary (MOEAD), Multi Objective Grasshopper Algorithm (MOGOA), Multi Objective Particle Swarm Optimization (MOPSO), Multi Objective Grey Wolf optimization (MGWO) algorithm and Improved Multi Objective Differential Evolution (IMODE) algorithms are considered for the designing the multi-objective WSN and their performances are compared based on the scalability, lifetime, energy distribution and cluster distribution of sensor nodes. The simulation results demonstrated that MOEAD based WSN achieves 24.2% more life time than second best algorithms. Further, it achieves 97% (in average) accuracy in cluster quality. Moreover, MOEAD outperformed than other algorithms in Pareto optimal solution in terms of convergence, diversity and solution quality.

Wireless Sensor Networks (WSNs) have drawn a lot of interest from a variety of industries, such as industrial automation, healthcare, and environmental monitoring. Typically, these networks are made up of sensor nodes that run on batteries and depend on energy-efficient operation to extend their lifetime. Renewable and sustainable energies are suitable for wireless sensor networks. Energy harvesting from dispersed renewable sources, such as solar, wind, biomass, and vibration, has emerged as a possible approach to alleviate the limits associated with limited battery life. The state-of-the-art methods and difficulties associated with energy harvesting in wireless sensor networks (WSNs) from a variety of distributed renewable sources, such as solar, wind, vibration, and temperature gradients, are thoroughly reviewed in this study. The paper discusses the many techniques for extracting and converting energy from these sources, highlighting the benefits and drawbacks of each. This paper explores several energy harvesting techniques and challenges. The study also discusses the difficulties in integrating energy harvesting, including adaptive power management, energy forecast, intermittent energy supply, and integration issues. The assessment also highlights research gaps and potential future initiatives in the field of energy harvesting from renewable sources. Researchers, technologists, and policymakers working in the fields of renewable energy and wireless sensor networks would find this thorough assessment to be quite insightful. It illuminates how energy harvesting technologies may improve sensor network autonomy and sustainability, leading to breakthroughs in environmental monitoring and other vital applications. The development of sustainable, independent, and effective sensing systems is greatly aided by the investigation of methods and obstacles in energy harvesting for wireless sensor networks. In addition to addressing current issues, this research opens doors for innovation, fostering a more sustainable approach to data collection and monitoring, and having a positive effect on a number of industries.

In this paper we present a Table less Position based Routing (TPR) scheme for low power data centric wireless sensor networks. TPR is localized, uses greedy forwarding approach, and does not rely on neighborhood information. These characteristics reduce the communication overhead (no neighborhood information exchange), make the protocol highly scalable (no routing tables are maintained and beacons are not exchanged when a node leaves or enters the networks), and it performs better in mobile environments (as the next hop is non-deterministic and is computed at real time). It also deals with dead end problems by a recovery strategy in a distributed and localized way. TPR is implemented in the OMNET++ based discrete event simulation environment PAWiS (simulation framework for low power wireless sensor networks). The results show that TPR provides guaranteed delivery, extended network life time, and a mechanism to route on the basis of end to end delay and/or energy consumption.

Wireless Sensor Networks (WSNs) are indispensable for current applications, such as smart cities, industrial automation and environment monitoring. However, the performance of these networks is heavily dependent on the routing protocols used, especially given the strict limitations on energy, memory, and processing power in sensor nodes. This study presents a detailed evaluation of routing protocols designed for resource-constrained WSN nodes with a focus on energy efficiency, computation overhead, communication performance, scalability and security. A comparable study for the most popular routing protocols such as LEACH, AODV, DSDV, PEGASIS, and GPSR was performed to highlight the compatibility of such protocols with variety of WSN applications. Various optimization techniques to enhance the efficiency of the protocol are also introduced based on adaptive duty cycles, hierarchical clustering, hybrid routing paradigms, and lightweight security mechanisms. The vision and insights of this paper are to offer a good and well-organized ground for enabling and refining the selection of routing protocols for WSN implementation in the complex world of reality.

Wireless Sensor Network (WSN) is an emerging wireless communication networks to provide potential secure optimized data routing between source and destination. Presently the performance analysis of the Wireless Sensor Network routing technique and security protocols is the major research issues. A major issue in wireless sensor network (WSN) is the energy constraint in a node and its limited computing resources, which may pose an operational hazard or limitations on the network lifetime. Therefore, analyses of innovative secure routing techniques are required to utilize the resources of WSN to improve the life time of network in WSN. To design & develop any innovative wireless sensor network routing and security protocol, the network characteristics & its design issues are important consideration. The proposed spherical Grid Routing Protocol (SGRP) is designed and developed to enhance Quality of service to improve WSN performance using NS2 simulator compare to compared to modified LEACH techniques. The packet transmitted, packet received, average energy, throughput and packet delivery ratio are the main common performance measures that are used for performance analysis of proposed SGRP protocols.

The goal of Service-Oriented Architectures (SOA) is to enable easy cooperation of a large number of computers and orchestration of services that are connected via a network. However, SOA for wireless sensor networks (WSN) and cyber-physical systems (CPS) is still a challenging task. Consequently, for design and development of large CPS like WSNs connected to clouds, SOA has not yet evolved as an integral technology. One of the limiting issues is service registration and discovery. In large CPS discovery of services is tedious, mostly due to the fact that services are often semantically bound to a region or an application function while SOA forces service endpoints to be based on addresses of nodes. Also, today, SOA technologies are not used for service composition within sensor nodes and between sensor nodes, and even worse, different methods exist for service access in a WSN and in the backend. Therefore, service development differs largely in WSN and cloud. To overcome this limitation, we suggest a name-centric service architecture for cyber-physical systems. Our architecture is based on (a) using URNs instead of URLs to provide a service-centric architecture instead of service-or location-centric networking, (b) using the well-known CCNx protocol as a basis for our architecture which supports location and access transparency, and (c) employing CCN-WSN as the resource-efficient lightweight implementation for WSNs to build a name-based service bus for CPS. We evaluate the architecture by implementing an example application for facility management.

Radio frequency identification (RFID) and wireless sensor networks (WSNs) have been popular in the industrial field, and both have undergone dramatic development. RFID and WSNs are well known for their abilities in identity identification and data transmission, respectively, and hence widely used in applications for environmental and health monitoring. Though the integration of a sensor and an RFID tag was proposed to gather both RFID tag and sensed information, few previous research efforts explore the integration of data transmission modes in the RFID and WSN systems to enhance the performance of the applications. In this paper, we propose a hybrid RFID and WSN system (HRW) that synergistically integrates the traditional RFID system and WSN system for efficient data collection. HRW has hybrid smart nodes that combine the function of RFID tags, the reduced function of RFID readers, and wireless sensors. Therefore, nodes can read each other's sensed data in tags, and all data can be quickly transmitted to an RFID reader through the node that first reaches it. The RFID readers transmit the collected data to the back-end servers for data processing and management. We also propose methods to improve data transmission efficiency and to protect data privacy and avoid malicious data selective forwarding in data transmission. Comprehensive simulation and trace-driven experimental results show the high performance of HRW in terms of the cost of deployment, transmission delay and capability, and tag capacity requirement.

Wireless Sensor Networks (WSNs) have permeated various domains, such as industrial automation, healthcare, and environmental monitoring, due to their ability to effectively collect and communicate data in diverse environments. The reliability and durability of WSNs have become paramount, particularly in critical applications. Predictive maintenance, which anticipates faults and mitigates them preemptively, emerges as a pivotal strategy to enhance network longevity and reliability. This paper explores using machine learning (ML) approaches for predictive maintenance in WSNs. Various ML algorithms, including regression, classification, and clustering, are investigated in terms of their efficacy in predicting and diagnosing issues within the network, thereby facilitating timely interventions. The paper contributes a novel architecture integrating ML models into WSNs to monitor, analyze, and predict potential failures, ensuring optimal network functionality.

This paper presents a survey on the current state-of-the-art in Wireless Sensor Network (WSN) Operating Systems (OSs). In recent years, WSNs have received tremendous attention in the research community, with applications in battlefields, industrial process monitoring, home automation, and environmental monitoring, to name but a few. A WSN is a highly dynamic network because nodes die due to severe environmental conditions and battery power depletion. Furthermore, a WSN is composed of miniaturized motes equipped with scarce resources e.g., limited memory and computational abilities. WSNs invariably operate in an unattended mode and in many scenarios it is impossible to replace sensor motes after deployment, therefore a fundamental objective is to optimize the sensor motes’ life time. These characteristics of WSNs impose additional challenges on OS design for WSN, and consequently, OS design for WSN deviates from traditional OS design. The purpose of this survey is to highlight major concerns pertaining to OS design in WSNs and to point out strengths and weaknesses of contemporary OSs for WSNs, keeping in mind the requirements of emerging WSN applications. The state-of-the-art in operating systems for WSNs has been examined in terms of the OS Architecture, Programming Model, Scheduling, Memory Management and Protection, Communication Protocols, Resource Sharing, Support for Real-Time Applications, and additional features. These features are surveyed for both real-time and non-real-time WSN operating systems.

Pattern transformation-invariant schemes for wireless sensor networks based on an edge detection gradient-based mechanism

Wireless sensor network (WSN) is a developed wireless network consisting of some connected sensor nodes. The WSN is employed in many fields such as military, industrial, and environmental monitoring applications. These nodes are equipped with sensors for sensing the environmental variables such as temperature, humidity, wind speed, and so on. In most applications, WSN is positioned in remote places and harsh environments, where they are most probably exposed to faults. Hence, faulty sensor identification is one of the most fundamental tasks to be considered in WSN. This study suggests a hybrid methodology based on mutual information change (MIC) and wavelet transform (WT) for faulty sensor identification. The MIC method is suggested to study correlation among sensors, while the WT technique is proposed for self‐sensor detection. WT is suitable for analysing non‐stationary signals into approximation and detail coefficients. The suggested algorithm performance is investigated by applying a real case study at an arbitrary location close to Cairo, Egypt. The results of each method are compared using the true positive rate (TPR), false negative rate, and accuracy measures. Obtained results have shown that combining MIC and WT techniques can achieve a higher TPR and accuracy reach 100% in most fault types.

Smart parking is common in contemporary cities. These smart parking lots are outfitted mostly with wireless sensor networks (WSNs), which are used to detect, monitor, and collect data on the availability status of all existing parking spaces in a given area. Sensors make up WSN, which may gather, process, and transmit informations to the sink. However, the power and communication limitations of the sensors have an effect on the performance and quality of the WSNs. The decrease in the battery and the energy of the nodes causes a decrease in the life of the nodes and also of the entire WSN network. In this article, we present a routing protocol that implements an efficient and robust algorithm allowing the creation of clusters so that the base station can receive data from the entire WSN network. This protocol adopts a reliable and efficient algorithm allowing to minimize the energy dissipation of the sensors and to increase the lifetime of the WSN. In comparison to alternative parking lot management protocols already in use, the simulation results of the proposed protocol are effective and robust in terms of power consumption, data transmission reliability, and WSN network longevity.

It would be useful if an IP-based host could collect information from multiple remote data-centric networks using a predefined application programming interface (API). This research presents two APIs for gathering named data from remote WSNs - one API for wireless sensor nodes in the data-centric directed diffusion WSN and the other API for IP-based nodes residing on an IP network outside of the WSN. An associated WSN middleware layer used to effectively connect these two APIs through an application-layer gateway is also described. These three components work together in harmony to support gathering of sensed data from one or more remote WSNs through an application-layer gateway which connects different remote WSNs to an IP network.