Markov-based algorithms for wireless sensor network: Theoretical insights and python implementation

Automatic localization is one of the major issues in Wireless Sensor Networks (WSN). DV-hop algorithm is a well-known localization algorithm in WSN but with limited localization accuracy. In this paper, an improved DV-hop localization algorithm in hybrid optical wireless sensor networks is proposed based on the optimization of the parameters in WSN. Various factors that affect the localization accuracy of the DV-hop algorithm in WSN are investigated, including the communication radius of the node, the number of beacon nodes and the number of the total nodes. As the DV-hop algorithm is applied into hybrid optical sensor and WSNs (O-WSN) with rectangular topology, different parameters have to be optimized accordingly. Simulation results show that the square topology outperforms the rectangle topology more than 45 % under the same network parameters using the improved DV-hop algorithm. Therefore another improved DV-hop called Sub-Square Weighted DV-hop (SSW DV-hop) is proposed for the rectangle topology. Both simulation and experiment results demonstrate that applying the SSW DV-hop algorithmin O-WSNs could significantly improve the localization accuracy.

In this work we will develop an extension of one of existing routing algorithm in wireless sensor network. This new adaptation will permit the sensor node to save more energy and transmit images in wireless mode. This situation will be strategic and helpful especially in disaster scenario, where groups of rescuers must be on site to accomplish emergency tasks; therefore it's very important and necessary to establish a wireless communication in real time between individuals or groups. The nature of wireless video sensor network makes it suitable to be used in the context of emergencies because introducing a video give more information in precise time and this is very advantageous when the existing infrastructure is down or severely overloaded. In emergencies the network topology may change rapidly and randomly. The increasing mobility of terminals makes them progressively dependent on their autonomy from the power source. This is illustrated by introducing many mobility models and using many scenarios of mobility in emergency situation, where image transmission via sensor node is used. Low complexity algorithm in image processing in order to reduce time transfer of selected data by this way allows saving energy. Efficiency in emergency scenario is the main objective of this work, achieved by the combination of three strategies: low-power mode algorithm, a power-aware routing strategy and compression technique in image processing used in sensor node. A selected set of simulations studies and real test bed on sensor node platform (Telos-B) indicate a reduction in energy consumption and a significant increase in node lifetime whereas network performance is not affected significantly. This is the big interest of our work in emergency situation, by increasing life time of node, individual can communicate longer and give more chance to rescuers to find them.

Data collection is a key operation in wireless sensor networks. In view of the privacy-preserving problem of the existing data collection schemes, this paper proposes a secure and verifiable continuous data collection algorithm (SVCDC) in wireless sensor networks. Taking the temporal correlation of sensory data into consideration, SVCDC reconstructs multiple sensory data in one period, which can effectively decrease data traffic, and then by encrypting the reconstructed data, SVCDC ensures the privacy of the sensory data. In addition, in SVCDC, the “fingerprint” of sensory data is generated, aggregated and transmitted to Sink. Then, Sink extracts the “fingerprint”, which ensures the verification of the integrity and timeliness of each node’s sensory data and the detections of attack such as replaying or discarding data. Since the “fingerprint” is aggregated and much shorter than the original data, the communication cost of “fingerprint” is low. Theoretical analysis and experimental results show that SVCDC has advantages in traffic.

Sensing tasks should be allocated and processed among sensor nodes in minimum times so that users can draw useful conclusions through analyzing sensed data. Furthermore, finishing sensing task faster will benefit energy saving, which is critical in system design of wireless sensor networks. To minimize the execution time (makespan) of a given task, an optimal task scheduling algorithm (OTSA-WSN) in a clustered wireless sensor network is proposed based on divisible load theory. The algorithm consists of two phases: intra-cluster task scheduling and inter-cluster task scheduling. Intra-cluster task scheduling deals with allocating different fractions of sensing tasks among sensor nodes in each cluster; inter-cluster task scheduling involves the assignment of sensing tasks among all clusters in multiple rounds to improve overlap of communication with computation. OTSA-WSN builds from eliminating transmission collisions and idle gaps between two successive data transmissions. By removing performance degradation caused by communication interference and idle, the reduced finish time and improved network resource utilization can be achieved. With the proposed algorithm, the optimal number of rounds and the most reasonable load allocation ratio on each node could be derived. Finally, simulation results are presented to demonstrate the impacts of different network parameters such as the number of clusters, computation/communication latency, and measurement/communication speed, on the number of rounds, makespan and energy consumption.

Energy efficiency and cost reduction are critical challenges in the design and operation of wireless sensor networks (WSNs). This paper proposes ECO-WOA, an enhanced Whale Optimization Algorithm tailored for simultaneous minimization of energy consumption and operational cost in WSNs. By integrating a balanced exploration-exploitation strategy with a localized search mechanism, ECO-WOA effectively navigates the complex optimization landscape to identify optimal sensor configurations. Experimental evaluations demonstrate that ECO-WOA significantly outperforms baseline approaches in reducing total energy expenditure and deployment costs while maintaining network performance. The algorithm's convergence behavior and solution quality are validated through extensive simulations, showcasing its potential as a robust and computationally efficient optimization tool for resource-constrained wireless systems. Future extensions of ECO-WOA may incorporate additional constraints and objectives, further enhancing its applicability in practical WSN deployments.

High efficient routing is an important issue in the design of limited energy resource wireless sensor networks (WSNs). This chapter presents an Improved Energy-Efficient Ant-Based Routing Algorithm (IEEABR) in wireless sensor networks. Compared to traditional Basic Ant-Based Routing (BABR), Improved Ant-Based Routing (IABR), and Energy-Efficient Ant-Based Routing (EEABR) approaches, the proposed IEEABR approach has advantages of reduced energy usage and achieves a dynamic and adaptive routing that can effectively balance the WSN node power consumption and increase the network lifetime. This chapter covers applications and routing in WSNs, different methods for routing using ant colony optimization (ACO), a summary of routing algorithms based on ant systems, and the Improved Energy-Efficient Ant-Based Routing Algorithm approach. Simulations results were analyzed while also looking at open research problems and future work to be done. The chapter concludes with a comparative summary of results with IABR and EEABR.

Energy is an important consideration in the design and deployment of wireless sensor networks (WSNs) since sensor nodes are typically powered by batteries with limited capacity. Since the communication unit on a wireless sensor node is the major power consumer, data compression is one of possible techniques that can help reduce the amount of data exchanged between wireless sensor nodes resulting in power saving. However, wireless sensor networks possess significant limitations in communication, processing, storage, bandwidth, and power. Thus, any data compression scheme proposed for WSNs must be lightweight. In this paper, we present an adaptive lossless data compression (ALDC) algorithm for wireless sensor networks. Our proposed ALDC scheme performs compression losslessly using multiple code options. Adaptive compression schemes allow compression to dynamically adjust to a changing source. The data sequence to be compressed is partitioned into blocks, and the optimal compression scheme is applied for each block. Using various real-world sensor datasets we demonstrate the merits of our proposed compression algorithm in comparison with other recently proposed lossless compression algorithms for WSNs.

Energy efficiency is critical in the design and deployment of wireless sensor networks. Data compression is a significant approach to reducing energy consumption of data gathering in multi-hop sensor networks. Existing compression algorithms, however, only apply to either lossless or lossy compression, but not to both. This paper presents a unified algorithmic framework to both lossless and lossy data compression, thus effectively supporting the desirable flexibility of choosing either lossless or lossy compression in an on-demand fashion based on given applications. We analytically prove that the performance of the proposed framework for lossless compression is superior to or at least equivalent to that of traditional predictive coding schemes regardless of any entropy encoders used. We demonstrate the merits of our proposed framework in comparison with other recently proposed compression algorithms for wireless sensor networks including LEC, S-LZW and LTC using various real-world sensor data sets.

In wireless sensor network, energy consumption is one of the most important issues. For every application like monitoring temperature, humidity and pressure, sensor nodes need to keep active which consumes lot energy. The nodes once deployed are not easily replaceable and rechargeable. So, adapting duty cycle of sensor node is a good idea to reduce energy consumption. The duty-cycle is a method that periodically repeats sleep and wake periods. The goal of this research is to design adaptive duty cycle algorithm for wireless sensor networks that minimizes energy usage. The algorithm is designed for both high as well as low traffic so that performance of network can be analyzed under variable traffic load. In this, the sleep time of node will be adjusted in such a way that it should get active only when there is event in the network. Due to its adaptive nature, the energy consumption will be lesser which will help improves network lifetime.

A Wireless Sensor Network (WSN) is a network composed of sensor nodes communicating among themselves and deployed in large scale (from tens to thousands) for applications such as environmental, habitat and structural monitoring, disaster management, equipment diagnostic, alarm detection, and target classification. In WSNs, typically, sensor nodes are randomly distributed over the area under observation with very high density. Each node is a small device able to collect information from the surrounding environment through one or more sensors, to elaborate this information locally and to communicate it to a data collection centre called sink or base station. WSNs are currently an active research area mainly due to the potential of their applications. However, the deployment of a large scale WSN still requires solutions to a number of technical challenges that stem primarily from the features of the sensor nodes such as limited computational power, reduced communication bandwidth and small storage capacity. Further, since sensor nodes are typically powered by batteries with a limited capacity, energy is a primary constraint in the design and deployment of WSNs. Datasheets of commercial sensor nodes show that data communication is very expensive in terms of energy consumption, whereas data processing consumes significantly less: the energy cost of receiving or transmitting a single bit of information is approximately the same as that required by the processing unit for executing a thousand operations. On the other hand, the energy consumption of the sensing unit depends on the specific sensor type. In several cases, however, it is negligible with respect to the energy consumed by the communication unit and sometimes also by the processing unit. Thus, to extend the lifetime of a WSN, most of the energy conservation schemes proposed in the literature aim to minimize the energy consumption of the communication unit (Croce et al., 2008). To achieve this objective, two main approaches have been followed: power saving through duty cycling and in-network processing. Duty cycling schemes define coordinated sleep/wakeup schedules among nodes in the network. A detailed description of these techniques applied to WSNs can be found in (Anastasi et al., 2009). On the other hand, in-network processing consists in reducing the amount of information to be transmitted by means of aggregation (Boulis et al., 2003) (Croce et al., 2008) (Di Bacco et al., 2004) (Fan et al., 2007) 1

Considering the poor localization accuracy when applying DV-Hop algorithm to node location in wireless sensor networks, an improved DV-Hop localization algorithm for wireless sensor networks, named iDV-Hop, is proposed in this article. In the proposed algorithm, anchor nodes refine their average hop-size by minimum mean square error and modify it by error factor. Then, the average hop-size between the unknown node and the anchor node is modified by a dynamic weight coefficient, which is related to the minimum hop number. Using this average hop-size and the minimum hop number, the distance among each unknown node and all anchor nodes can be calculated. Finally, the coordinates of unknown nodes are estimated by the 2D hyperbolic localization algorithm. To obtain higher localization accuracy, the unknown node upgrades its location by exploiting the obtained information in solving the system of equations. Simulations are carried out on three network models constructed by complex network theory and the analysis of the simulation results will actually help the engineers to deploy the proposed localization algorithm for the real-world wireless sensor network.

Wireless sensor networks (WSNs) are receiving an upsurge of research interest in both academia and industry. The key issue for the design and operation of WSNs is the optimization of power consumptions. Several approaches have been proposed to address this aspect and a very promising approach is known to be ¿clustering¿, which foresees to allow only a subset of nodes in the network to send data (via compress and aggregate operations) to a common sink node (e.g., for data reporting in monitoring application). Recently, a novel clustering algorithm based on the concept of ¿data similarity¿ has been introduced and shown to provide good performance. In the present paper, we move from and generalize this latter clustering algorithm, as well as substantiate via computer simulations the advantages of our solution with respect to the original one. In particular, we extend the concept of data similarity from the perfect match of measured (i.e., raw) data to the statistical correlation of them. We also introduce the semi-variogram metric as a sound measure to estimate the statistical correlation among measured data. The novel algorithm is termed Data Similarity Variogram-based Clustering Algorithm (DSVCA), which will be proven to be a good solution for network's data traffic minimization and for reducing the energy consumptions of the overall network.

Wireless sensor network coverage to a large extent depends on the deployment of wireless sensor networks. We propose in this work an approach based on an optimized artificial fish-swarm algorithm for wireless sensor networks deployment optimization scheme in a wireless sensor network which is composed of fixed sensor nodes and some mobile sensor nodes. The OAFSA (Optimized Artificial Fish Swarm Algorithm) inherits the foraging and rear behavior of artificial fish in AFSA (Artificial Fish Swarm Algorithm), and optimizes the capacity of finding maximal objective function value. The simulation results show that our approach works better than the AFSA does in wireless sensor network coverage, which improves the network performance.

Recent advances in the technology of wireless electronic devices have made possible to build ad–hoc Wireless Sensor Networks (WSNs) using inexpensive nodes, consisting of low–power processors, a modest amount ofmemory, and simplewireless transceivers. Over the last years, many novel applications have been envisaged for distributed WSNs in the area of monitoring, communication, and control. Sensing and controlling the environment by using many embedded devices forming a WSN often require the measured physical parameters to be associated with the position of the sensing device. As a consequence, one of the key enabling and indispensable services in WSNs is localization (i.e., positioning). Moreover, the design of various components of the protocol stack (e.g., routing and Medium Access Control, MAC, algorithms) might take advantage of nodes’ location, thus resulting in WSNs with improved performance. However, typical protocol design methodologies have shown significant limitations when applied to the field of embedded systems, like WSNs. As a matter of fact, the layered nature of typical design approaches limits their practical usefulness for the design of WSNs, where any vertical information (like, e.g., the actual node’s position) should be efficiently shared in such resource constrained devices. Among the proposed solutions to address this problem, we believe that the Platform–Based Design (PBD) approach Sangiovanni-Vincentelli (2002), which is a relatively new methodology for the design of embedded systems, is a very promising paradigm for the efficient design of WSNs. 1

An improved harmony search based energy-efficient routing algorithm for wireless sensor networks