Immune system based distributed node and rate selection in wireless sensor networks

Background: Event detection and monitoring applications involve highly populated sensor nodes in Wireless Sensor Networks (WSNs). Dense deployment of nodes leads to correlated sensor observations in the spatial and temporal domain. Most of the previous works focused on constant sensing radii for spatially correlated sensor observations. However, in real time scenario, the sensor nodes may have variable sensing coverage areas, which comprise a Heterogeneous WSN. background: Spatial correlation model discussed in prior literature focused on Homogeneous sensing of sensor nodes. But, real time scenario the condition changes due to interferences obstructing sensing areas. Also, different manufacturers may provide different specifications for sensing areas, thus resulting into Heterogeneous sensing. Objective: To address this issue, we present an Enhanced Weighted Spatial Correlation Model for Heterogeneous sensor nodes in WSNs. Method: The mathematical framework considers the spatial coordinates of sensor nodes, the distances between the sensor nodes, and their sensing coverage. Furthermore, the correlation coefficient is calculated in terms of overlapping areas for randomly deployed nodes. Performance of the correlation model is evaluated and analyzed in terms of event distortion function. In addition to this, a macro and micro-zone concept is introduced, wherein sensor information is weighted for better event estimation at the sink node. Moreover, dynamic weighing of nodes like Inverse, Shepard’s and Gaussian distance weighing algorithms are simulated and analyzed for minimal event distortion. Over and above, the system performance is evaluated for different approaches considering reporting nodes with and without clustering of sensor nodes for macro and micro-zone concept. Simulation results for the Enhanced Weighted Spatial Correlation Model developed are obtained using MATLAB software. method: In order to evaluate the performance of the enhanced correlation model considering Macro and Micro-zone concept, simulations are carried out inMATLAB. Simulations are performed for ‚ trials and averaging of the values are finally used for analysis of results. Results: The comparative study shows an improved system performance in terms of minimal distortion obtained for non-clustered nodes; thereby reducing the computational complexity of cluster formation. Furthermore, the dynamic weighing algorithms outperform the existing fixed weighing algorithms for the correlation model with the lowest distortion function. Conclusion: Moreover, in the above algorithms, the event distortion gradually decreases and later becomes constant with the increase in the number of representative nodes. Hence, it illustrates that minimal distortion can be achieved by activating lesser number of representative nodes, thereby preserving the energy of other sensor nodes and increasing the lifetime of WSNs.

Wireless sensor networks (WSNs) that periodically collect the environmental information such as temperature, humidity, and so on require the coverage of a given target field anytime and the operation lifetime longer than an expected duration by the minimum number of sensor nodes. For these WSNs, we propose a method deciding a schedule of node movement to cover the target agricultural field from plant to harvest by the minimal number of nodes in order to reduce the node deployment cost. The proposed method computes a moving schedule of each mobile sensor node so that all the nodes cover the target field without depleting battery of some of the nodes by predicting solar power generation at each point of the target field where shadow areas change depending on time, orbit of the sun, and height of crops. We conducted computer simulations and compared the performance of the proposed method with a conventional method. As a result, our method achieves 4% reduction of the number of nodes and 10% extension of the operation lifetime compared to the method without estimation of power generation amount.

Finding and identifying the minimal number of sensor nodes for a sensor network is one of the most basic problems for the implementation of distributed state estimators. Despite a plethora of research studied sensor networks, most of them ignored this problem or assumed the considered sensor network comes with an ideal number of sensor nodes. We revisit this problem in the current paper. To this end, the minimal number of sensor nodes problem is first formalized and a novel observability condition, namely, minimal nodes uniform observability (MNUO), is then proposed. Next, this MNUO is applied to study the stability issues of the distributed Kalman filtering algorithm. In what follows, under the condition of MNUO, conditions to ensure its stability are given and the results about the relation of the filtering performance before and after selecting the minimal number of sensor nodes are obtained. Finally, optimization solutions and an example are given to find the minimal number of sensor nodes for a sensor network.

Conventional target tracking systems are based on powerful sensor nodes, capable of detecting and locating a target in a large deployment area but most systems require high transmission power levels and a large volume of messages, much time for neighbor discovery operations, and many sensors to detect the target at a given time. We propose a two‐level cooperative and energy‐efficient tracking algorithm (CET) that reduces energy consumption by requiring only a minimum number of sensor nodes to participate in communication, transaction, and perform sensing for target tracking in wireless sensor networks. It is expected that only the nodes adjacent to the target are responsible for observing the target to save the energy consumption and extend the network lifetime as well by using a wakeup mechanism and a face‐aware routing. Through performance analysis and simulation studies, we demonstrate that CET improves target capturing speed and outperforms some existing protocols of target tracking with energy saving under certain ideal situations. Copyright © 2009 John Wiley & Sons, Ltd.

A scheme for WSNs (wireless sensor networks) security is given by dividing sensing tetrahedron into clusters and using the symmetric polynomials in this paper.The sensing tetrahedron is divided into a number of small grids.All those sensor nodes, both ordinary sensor nodes and heterogeneous sensor nodes are distributed in the sensing tetrahedron.In a grid, all ordinary sensor nodes and heterogeneous sensor nodes establish their shared keys through using the symmetric polynomials.All the heterogeneous sensor nodes establish their shared keys through using the symmetric polynomials.At last, all sensor nodes establish their keys directly or indirectly in the whole sensing tetrahedron.Analysis and comparison demonstrate this scheme enhances the WSN security, has good network connectivity, saves node storage, reduces network computing load, and extends the network lifetime.

Existing work on placing additional relay nodes in wireless sensor networks to improve network connectivity typically assumes homogeneous wireless sensor nodes with an identical transmission radius. In contrast, this paper addresses the problem of deploying relay nodes to provide fault-tolerance with higher network connectivity in heterogeneous wireless sensor networks, where sensor nodes possess different transmission radii. Depending on the level of desired fault-tolerance, such problems can be categorized as: (1) full fault-tolerance relay node placement, which aims to deploy a minimum number of relay nodes to establish k (k ges 1) vertex-disjoint paths between every pair of sensor and/or relay nodes; (2) partial fault-tolerance relay node placement, which aims to deploy a minimum number of relay nodes to establish k (k ges 1) vertex-disjoint paths only between every pair of sensor nodes. Due to the different transmission radii of sensor nodes, these problems are further complicated by the existence of two different kinds of communication paths in heterogeneous wireless sensor networks, namely two-way paths, along which wireless communications exist in both directions; and one-way paths, along which wireless communications exist in only one direction. Assuming that sensor nodes have different transmission radii, while relay nodes use the same transmission radius, this paper comprehensively analyzes the range of problems introduced by the different levels of fault-tolerance (full or partial) coupled with the different types of path (one-way or two-way). Since each of these problems is NP-hard, we develop O(sigmak <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> )-approximation algorithms for both one-way and two-way partial fault-tolerance relay node placement, as well as O(sigmak <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> )-approximation algorithms for both one-way and two-way full fault-tolerance relay node placement (sigma is the best performance ratio of existing approximation algorithms for finding a minimum k-vertex connected spanning graph). To facilitate the applications in higher dimensions, we also extend these algorithms and derive their performance ratios in d-dimensional heterogeneous wireless sensor networks (d ges 3). Finally, heuristic implementations of these algorithms are evaluated via simulations.

We study the scaling capacity of wireless sensor networks in case of multiple gateways are presented. In such a heterogeneous network, many gateways are needed to gather data from the sensor nodes in network. Different from the previously studied single-gateway model, our proposed gateway access model allows multiple gateway nodes being seamlessly connected with high speed wire-line or wide-band radios. Once the data from the sensor node is sent to any gateway node, it can be collaboratively sent to the appropriate sink node among multiple gateways, which could effectively avoid throughput reduction due to multi-hop routing among the sensor node. This mechanism is called ``Destination-hub", which could ensure the bounded time delay and network capacity. However under such a cooperative scheme, two research issues need to be tackled. One is the number of nodes in network and the second is the scaling capacity for each node when multiple gateway nodes are presented. Our proposed model is a fundamental research work for scaling capacity, which could also provide an instructive proposal for future gateway deployment. In this paper, we characterize the scaling capacity between gateways and sensor nodes under different gateway access model. If gateway nodes are randomly deployed, the number is Omega(log n), and there are at least 4 log l gateway nodes are needed in each squarelet region so as to keep the gateway connectivity, where r is the transmitting range of sensor nodes, and l is the side length of the deployed square region. And we can conclude that, the gateway nodes can be tightly bounded by Theta(log n). For random placement, the scaling capacity for such a scheme is (n). And we have also proposed an optimized scheme for reducing the number of gateway nodes. Considering the interference Voronoi cell, the reduced gateway nodes can be 2 lnl, and still bounded by Theta(log n).

A general wireless sensor network (WSN) is a system composed of a large number of sensor nodes. In the WSN system, all sensor nodes are responsible for sensing the interested events and transmitting the related information back to the sink node. The sink node acts like the brain of the whole WSN system for processing the collected data and giving control commands. The position of the sink node directly affects the data transmission efficiency and the distance from the terminal sensor node to the sink node. Thus, choosing the correct place to place the sink node in the whole network is a delicate work. In this paper, we propose a sink node placement method by proposing a newly designed object function and utilizing Cat Swarm Optimization (CSO) to find out the near best location for placing the sink node. The simulation results indicate that our proposed method presents good performance compared with the conventional Particle Swarm Optimization (PSO) algorithm in reducing the overall energy consumption and prolongs the lifetime of network in an efficient way.

Due to adverse effects of hostile deployment environment(s), sensors nodes (SNs) deployed in the network may drain or get damaged. This attributes for loss of connectivity between the sensors and the sink node. Hence it is necessary to design the wireless sensor network (WSN) in a manner that the network is capable of coping up with the failure of few nodes/links. Using single hop data communication model reduces the lifetime of the network as the SNs are resource constrained (in terms of battery). Relay nodes (RNs) facilitate in improving network lifetime, fault tolerance and/or connectivity. Finding minimum number of relay nodes for a fault tolerant, fully connected network is a NP Hard problem. In this paper, we follow two phase relay node placement procedure to achieve the objective. In the first phase, we cluster the SNs using mean shift algorithm and place RNs as the cluster heads. In the second phase, we use metaheuristic algorithms like Moth Flame Optimization (MFO), Differential Evolution (DE), Bat algorithm (BA) and Biogeography Based Optimization algorithm (BBO) to place the RNs such that the required fault tolerance is achieved along with fully connected network. Extensive simulation results prove that MFO algorithm performs better for fault tolerant relay node placement problem.

Discrimination of singleton and periodic attractors in Boolean networks

Environmental monitoring is a crucial application of wireless sensor networks (WSNs). This kind of application generally requires a certain geographical granularity of sensing. In addition, some applications simultaneously gathers sensor readings with different attributes. In this paper, we propose a topology construction method that prolongs the lifetime of a WSN which gathers multi-attribute sensor readings. In our proposed method, a minimum number of sensor nodes are selected to acquire sensor readings for each attribute that satisfies the application requirement. For gathering the acquired readings, our method constructs a tree-shaped topology considering the sum of residual energies of nodes and the granularity of sensing for each attribute. By doing so, our method avoids exhausting batteries of nodes which can acquire a specific attribute. We confirmed the effectiveness of our proposed method through simulation experiments.

In Mobile Wireless Sensor Networks (MWSNs) where mobile sensor nodes densely exist, it is desirable to gather sensor data from the minimum number of sensor nodes which are necessary to guarantee the sensing coverage in order to reduce communication traffic. In the past, we have proposed a data gathering method using mobile agents in dense MWSNs. However, since this method assumes that only one sink is present in a network, it cannot effectively reduce traffic in environments where multiple sinks exist. In this paper, we propose a mobile agents control scheme which guarantees multiple sinks’ coverages and efficiently gathers sensor data. In the proposed method, mobile agents are communalized if their sensing points overlap, and sensor data are aggregated to transmit them to same direction.

Sensor nodes in Wireless Sensor Networks (WSNs) operate with limited power resources such as small batterieswhich are difficult to be either recharged or replaced in some environments when depleted. Power consumptionrepresents one of the most constraints impact the design of WSNs, leading to various protocols and algorithmsaimed at minimizing the power consumption and extending batteries' lifetime. Sensor nodes in WSNs transmittheir periodic packets continuously to central nodes (receivers) which are responsible for decoding packets andtransmitting them to other communication networks. In addition, sensors usually follow various MAC strategieswhich allow accessing to wireless communication channels. However, sensors may attempt to access thewireless channels at the same time, potentially, leading to collisions among multiple nodes. In fact, central nodesin WSNs most often consume a large amount of power due to the necessity to decode every received packetregardless of the fact that the transmission may suffer from packets collision which impede the networkperformance. Therefore, in the receiver side of WSNs current collision detection mechanisms have largely beenrevolving around direct demodulation and decoding of received packets and deciding on a collision based onsome form of parity bits in each packet for error control. From information theoretic prospective full decoding ofreceived packets with error control bits at central nodes can achieve an efficient usage of network capacity,however, such an approach represents a major burden on power-constrained sensors. This drawback comes fromthe need to expend a significant amount of energy and processing complexity at sink nodes in order tofully-decode a packet, only to discover the packet is illegible due to a collision. In this paper, we propose a morepractical power saving approaches which achieve a significant power saving with low-complexity at the expenseof low throughput losses. Based on studying the statistics of received packets, central nodes can make a fastdecision to detect a collision without the need for full-decoding of the whole received packets. Our novelapproaches not only reduces processing complexity and hence power consumption, but it also reduces the delayincurred to detect a collision since it operates on only a small number of IQ samples in the beginning of areceived packet. In such a paradigm, our approaches operate directly at the output of the receiver’sAnalog-to-Digital-Converter (ADC) and eliminate the need to pass the corrupted packets through the entiredemodulator/decoder line-up. The performance gain of our proposed approach is illustrated through thecomparison between the computational complexity of our Statistical Discrimination (SD) approaches and someexisting Full Decoding (FD) algorithms(note 1). Our results show that the SD approaches has significant powersavings and low computational complexities over existing FD algorithms with low False-Alarm and Missprobabilities, which qualify our SD approaches to be considered as reliable collision detection mechanisms inWSNs. We also show how to tune various design parameters in order to allow a system designer multipledegrees of freedom for design trade-offs and optimization.

Now days, wireless technology is one of the center of attention for users and researchers. Wireless network is a network having large number of sensor nodes and hence called as “Wireless Sensor Network (WSN)”. WSN monitors and senses the environment of targeted area. The sensor nodes in WSN transmit data to the base station depending on the application. These sensor nodes communicate with each other and routing is selected on the basis of routing protocols which are application specific. Based on network structure, routing protocols in WSN can be divided into two categories: flat routing, hierarchical or cluster based routing, location based routing. Out of these, hierarchical or cluster based routing is becoming an active branch of routing technology in WSN. To allow base station to receive unaltered or original data, routing protocol should be energy-efficient and secure. To fulfill this, Hierarchical or Cluster base routing protocol for WSN is the most energy-efficient among other routing protocols. Hence, in this paper, we present a survey on different hierarchical clustered routing techniques for WSN. We also present the key management schemes to provide security in WSN. Further we study and compare secure hierarchical routing protocols based on various criteria.

This chapter presents a combined approach to visualize the IoT and cyber-physical system in any wireless communication networks. The brief overview of IoT and its applications in wireless sensor network (WSN) are elaborated in current era. In today’s present situation and the challenges faced during COVID-19 pandemic how the things can be monitored through IoT is being focused in this chapter. Also, the threats challenges and monitoring of cyber-physical system for security is presented in analysis form. WSN provides a bridge between the physical and virtual world and finds use in numerous applications like environmental monitoring, civil structural health monitoring and industrial process monitoring. Lately, it has found much use in the latest technology called Internet of Things (IoT). It seems that IoT has a great potential in shaping our future where everything will be connected together all the time. The major challenges faced by WSN include energy-efficiency, heterogeneity, and systematic design. WSN comprise of autonomous sensors which are capable of wirelessly transmitting their sensed parameter-values (also called packets) to remotely 188located sink node. The sink node is a WSN node which has the capability to assemble the data from all the nodes and forward it to farther distances to the remote base station through wired connections. The data transmission from source node to sink node is done by taking multi-hops which means that data is not sent directly to the destination node but through jumping from one intermediate node to another until the sink is reached. The sensor nodes also act as wireless routers which transmit data to other sensor-routers. So, sensor devices are very useful in deployment, collection of data, act as a IoT sensor devices as an application in technology. IoT is a device which can be used as application while a cyber-physical system is a collection of various mechanical components which are controlled and managed by different devices and algorithms. An IFM (Information Flow Monitor) is an application to backtrack queries and find the culprit in an error that has occurred in the cyber system. However, while backtracking and flow monitoring is not always guaranteed in terms of secure and efficient transaction. In some cases, the sender may deny the authority of the data so the information flow monitors may fail to provide a trustful and authentic source of data. Thus, the chapter focuses on both the issues of IoT integration with cyber-physical security (CPS) system. The key challenges and some solutions are proposed for the researchers for future work.