WSN architecture for smart irrigation system

Wireless charging is a greatly promising technology to resolve the energy limitation problem suffered by wireless sensor networks. In this paper, we propose an efficient mobile energy replenishment scheme based on hybrid mode, namely MERSH, which merges the advantages of the existing online and offline modes to improve the energy supplement efficiency and meanwhile to adapt well to the energy consumption dynamicity of sensors to achieve sustainable operation of wireless sensor networks. In MERSH, nodes are allowed to proactively send charging requests to the mobile charger, called MC, when the residual energy goes down a lower level. With a special-designed low-cost method, MERSH can plan an optimized path for MC every time when it charges the sensors that have sent charging requests. Moreover, to ensure energy supplement and avoid node failure due to the high dynamic energy consumption, MERSH dynamically adjusts the charging duration of each node along the calculated charging path according to its maximum tolerate charging latency and minimum charging wait time. The theoretic analysis shows that MERSH guarantees MC to move along the optimized charging path, which reduces the whole charging cost. Through extensive simulation results, we validate the effectiveness of the proposed MERSH.

The increasing in the demand for Wireless Sensor Networks (WSNs) has intensified studies which are dedicated to obtain more energy-efficient solutions, since the energy storage limitation is critical in those systems. Additionally, there are other aspects which usually must be ensured in order to get an acceptable performance of WSNs, such as area coverage and network connectivity. This paper proposes a procedure for enhancing the performance of WSNs: a multiobjective hybrid optimization algorithm is employed for solving the Dynamic Coverage and Connectivity Problem (DCCP) in flat WSNs subjected to node failures. This method combines a multiobjective global on-demand algorithm (MGoDA), which improves the current DCCP solution using a Genetic Algorithm, with a local on line algorithm (LoA), which is intended to restore the network coverage soon after any failure. The proposed approach is compared with an Integer Linear Programming (ILP)-based approach and a similar mono-objective approach with regard to coverage, network lifetime and required running time for achieving the optimal solution provided by each method. Results achieved for a test instance show that the hybrid approach presented can improve the performance of the WSN obtaining good solutions with a considerably smaller computational time than ILP. The multiobjective approach still provides a feasible method for extending WSNs lifetime with slight decreasing in the network mean coverage.

Wireless Sensor Networks (WSN) have been increasingly applied to industrial monitoring and control, where soft Quality of Service guarantees under worst-case scenarios are often required. Many performance analysis frameworks including Network Calculus are proposed to derive those guarantees. Those frameworks were originally designed for wired network and typically do not consider energy constraints, which play a crucial role in the operation of wireless sensor networks. In this paper, a novel model is proposed to integrate energy harvesting into the network calculus framework to stochastically bound the worst-case performance. In our framework, energy is converted into tokens to stochastically bound the service provided to the traffic. Meanwhile, behaviors of nodes during the situations of energy overflow and underflow are accurately characterized by two virtual queues. Various performance metrics are analyzed and bounded for a single node and for a tree-based data collection wireless sensor network. Additionally, procedures are presented to show the model instantiation for a specific wireless sensor network based on hardware parameters and empirically obtained energy harvesting traces.

Wireless sensor networks have become widely used in industrial automation systems due to their ease of deployment, flexibility, and cost-effectiveness. They provide real-time monitoring, diagnostics, and control of technological processes without the need for complex cabling infrastructure. However, the operation of wireless sensor networks in production environments is accompanied by risks associated with limited energy resources of nodes, the impact of electromagnetic interference, data transmission delays, the inability to guarantee communication stability, and vulnerability to cyberattacks. These threats are exacerbated in complex environments where equipment is located in hard-to-reach places, and the network has limited bandwidth and variable topology. Such conditions require the construction of multi-level structures for network health analysis, risk assessment, and adaptive management. The aim of the research is to develop two functional modules of a multi-level ontology:a risk assessment module and a network description module,that allow for an integrated analysis of threats, vulnerabilities, and wireless sensor network parameters, taking into account their changing state. The research methodology is based on the construction of formalized mathematical models: the network description is implemented through a graph representation with the parameters of nodes, connections, and their dynamics, while the risk assessment is performed using a multifactor model that takes into account theprobability of threats, the level of their impact, the criticality of network components, and external risks. The article presents examples of the application of modules based on typical production scenarios that demonstrate the relationship between the structural description of the network and the dynamic assessment of threats. Information from the network description module is used to calculate risks in real time, while the results of the risk analysis affect the dynamic adjustment of network operation parameters. The scientific novelty of the research lies in the integration of two formalized models within a single ontological system of risk-oriented wireless sensor network management. The practical significance lies in the possibility of implementing the proposed approaches in real industrial conditions to increase the reliability, flexibility, adaptability and information security of sensor networks. The proposed system is the basis for building self-adjusting smart networks capable of independently responding to changes in technical condition, threats and environmental parameters.

Now a days, it is essential, to improve the security including energy efficiency in various emerging application under WSN. To keep this in mind, a WDT node based sensor node master operations in wireless sensor network method is proposed. In this approach, network consists of random node deployment of sensor nodes and it is partitioned into levels. WDT is a watch dog timer based node acts as a station keeping node. In each level there is main selection and redundant selection of WDT node is considered. At a time one node is taken to monitor the other nodes in the level. By this all node health status is keep intact in different levels by the help of status word. Master operations includes, node information through status word, routing tree discovery for path used for data communication, efficient encryption technique in the path from source to sink via Key Distribution Centre(KDC). All this operations is carried out by WDT node and sensor nodes used in the path. The proposed approach also tells, how effectively energy level is maintained in the network. By implementing the WDT algorithm, routing tree and secured key based encryption methods the overall throughput, packet delivery ratio (PDR), End-to-End delay and energy may effectively manage.

Data gathering is a typical operation in wireless sensor networks where data flow through a data gathering tree towards a sink node. Plenty of work had been done on constructing energy-efficient data gathering trees at the routing layer. However, data generation in wireless sensor networks could be bursty as it is driven by the events of interest. Without any sleep scheme, the sensor nodes will stay idle for most of the time. Therefore energy-efficient data gathering trees alone are not enough for energy saving. We propose a routing layer data gathering sleep scheme (DGSS) which could be incorporated into existing data gathering tree formation algorithms to save unnecessary energy consumption due to idle listening and meanwhile adapts to bursty traffic condition. We compare DGSS with DMAC, a popular energy efficient MAC protocol for data gathering, through simulations and show that DGSS can perform better than DMAC for bursty traffic at relatively high data rates.

Convergecast is a fundamental operation in wireless sensor networks (WSNs). To support long-term deployment of WSNs, sensor nodes normally operate at low-duty-cycles. However, the low-duty-cycle operation significantly reduces the communication chance between nodes. Consequently, the risk of data collisions significantly increases when multiple senders transmit packets to a receiver during its very short active period. This problem further causes not only wasted packet retransmissions, but also a large delivery latency. Under such conditions, collision-free medium access is more appealing than recovering after collision for low-duty-cycle WSNs. In this work, we propose an <u>i</u>ncast-<u>co</u>llision-f<u>re</u>e convergecast protocol, named iCore, to address the many-to-one collision problem in low-duty-cycle WSNs. iCore employs the dynamic forwarding technique, establishes a non-conflicting schedule for efficient convergecast, and improves the channel utilization by allowing senders to opportunistically transmit packets once detecting unused slots. Specifically, we design efficient forwarder assignment and forwarding optimization algorithms that ensure low end-to-end latency under diverse data traffic types. Through comprehensive performance evaluations, we demonstrate that, compared with the baseline protocol, iCore effectively minimizes the end-to-end delay by 25&#x0025; &#x007E; 57&#x0025; and maintains high delivery ratio and energy efficiency for different many-to-one convergecast scenarios.

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.

With the success of wireless sensor networks (WSNs), traditional engineering and infrastructure industries are starting to develop solutions using WSN technologies. One of the main challenges of designing and developing WSNs for industrial monitoring and control is satisfying their strict reliability requirements. In this paper, we present a network-level reliability model, namely, end-to-end data delivery reliability (E2E-DDR), for estimating and optimizing the reliability performance of WSNs. In the E2E-DDR model, a framework is presented for capturing the mapping function between the packet reception ratio, background noise, and received signal strength (RSS). We use an alpha-stable distribution to accurately represent the background noise and a modified log-normal path loss model to more realistically describe the RSS. We also report a comprehensive performance evaluation performed by applying the E2E-DDR model in a real-world case study to estimate the network-level reliability and optimize the WSN deployment parameters. Note to Practitioners —The goal of this paper is to improve the estimation and optimization of the network-level reliability performance of industrial wireless sensor networks (WSNs) to satisfy their strict control requirements. In harsh industrial application scenarios, many factors affect radio link quality, such as RF transmit power, communication distance, and random background noise. Existing link quality estimation approaches mainly focus on providing a smoothed estimation of radio link quality without any solution for optimizing communication reliability to satisfy certain requirements. This paper suggests a new approach in which WSN nodes are used to measure and estimate the parameters of the scenario in which the nodes will be deployed and then to estimate and optimize the worst case reliability (which is a lower-bound value rather than a smoothed value) to ensure that the network is qualified. Through a real-world case study, we demonstrate how to estimate the lower bound on reliability and how to optimize the reliability by computing the maximum deployment distance between nodes as an example. The experiments suggest that this approach is feasible.

Exploring the tradeoff between energy dissipation, delay, and the number of backbones for broadcasting in wireless sensor networks through goal programming

Aggregation is an important and commonplace operation in wireless sensor networks. Due to wireless interferences, aggregation in wireless sensor networks often suffers from packet collisions. In order to solve the collisioa problem, aggregation scheduling is extensively researched in recent years. In many sensor network applications such as real-time monitoring, aggregation time is the most concerned performance. This paper considers the minimum-time aggregation scheduling problem in duty-cycled wireless sensor networks for the first time. We show that this problem is NP-hard and present an approximation algorithm based on connected dominating set. The theoretical analysis shows that the proposed algorithm is a nearly-constant approximation. Simulation shows that the scheduling algorithm has a good performance.

Broadcast is a fundamental operation in wireless sensor networks and plays an important role in the communication protocol design. This paper investigates the Minimum Latency Broadcast Scheduling in Duty Cycle (MLBSDC) problem in wireless sensor networks, which is known to be NP-hard. Recent studies in this area focus on giving collision-free scheduling and finding the upper bound of broadcast latency. The previously best-known algorithm for MLBSDC, OTAB algorithm, has an approximation ratio of 17|T| where |T| denotes the number of time slots in a working period. In this paper, we present an improved algorithm combining more transmissions into one working period, called Latency Aware Broadcast Scheduling (LABS) algorithm, to reduce the broadcast latency. Theoretical analysis proves that the LABS achieves the approximation ratio of 12|T| while keeping the same complexity with OTAB. We also conduct extensive simulations to show that our proposed algorithm achieves up to 34% and 40% performance improvement over existing schemes in terms of delay time and number of transmissions, respectively.

One of the most critical damaging effects on most wireless sensor networks is a flooding attack. Flooding threat occurs not only from spoofed packets generators launched by intruders but also from the inherent behavior of the network protocols. Flooding harm can be categorized into two types as follows. First, a sensor cannot provide required services when it receives a lot of packets. An essential part of packets are rejected and packets delay becomes inadmissible. And second, if a sensor transmits many packets then the sensor battery is quickly exhausted. Energy harvesting technologies offers the potential of reducing the second vulnerability. This paper explores the operation of wireless sensor networks with energy harvesting capability taking into account both flooding negative effects above. We consider the feasibility and threats of the attacks and their impacts. We then propose a theoretical analysis of the flooding attacks. The corresponding mathematical model is offered. Counteracting methods against such intrusions is discussed.

The network model assumed in this paper consists of tiny, energy-constrained, commodity sensors massively deployed alongside with one or more sink nodes that provide the interface to the outside world. The sensors in the network are initially anonymous and unaware of their location. Our main contribution is to propose a new robust and energy-efficient solution for secure operation of wireless sensor networks. The paper motivates a new paradigm where security is based upon using parameterized frequency hopping and cryptographic keys in a unified framework to provide differential security services for wireless sensor networks.

Wireless sensor networks (WSNs) enable large-scale event monitoring; however, their performance is often constrained by low throughput. This study aims to develop a cluster-based routing protocol by implementing the multi-hopping clustering partition (MHCP) method. The MHCP process consists of three main stages: (i) cluster head (CH) selection, (ii) evaluation of node proximity to their respective CHs, and (iii) cluster partitioning to reduce intra-cluster variation. Four clusters were formed and interconnected through multi-hop communication, achieving throughput values of 142.0033, 244.1318, 119.0804, and 305.6159, respectively. In addition to the development of MHCP, the scientific contribution of this study is strengthened through the integration of the LEACH protocol and the K-means algorithm as a complementary methodological approach. LEACH improves energy efficiency through adaptive CH rotation, while K-means optimizes spatial node grouping. The combination of these methods ensures a balance between energy consumption and spatial proximity, resulting in improved throughput and extended network lifetime. Experimental results demonstrate that the proposed MHCP protocol achieves higher throughput than the conventional LEACH protocol across all clusters while maintaining acceptable delay and packet loss. These findings confirm that the integration of multi-hop communication and cluster partitioning effectively enhances data transmission efficiency and overall network performance in WSNs.