Wireless Sensor Networks: Deployment

In wireless sensor networks (WSNs), there are many factors, such as the reciprocal interference of wireless links, battlefield applications and nodes are exposed to poor physical protection medium. All of these result in the sensor nodes being more exposed to be attacked and compromised. Since sensors are normally energy limited in WSNs, it is constantly basic to conserve the node energy and expand the lifetime of the network. Besides, hierarchical clustering is deemed as one of the numerous ways to deal with the reduction of energy consumption of nodes and increasing the network lifetime in WSNs. This paper conspicuously introduces an efficient and secure malicious node detection model based on a hybrid clustering network for WSNs (ESMCH) which is based on a trusted mobile node. WSNs are still suffering from some attacks like Man-in-the-Middle Attack and Black hole Attack, so by using the ESMCH model, we can avoid these attacks. Finally, simulation results also indicate that the proposed model can increase the network lifetime, and achieve an efficient and secure clustering network.

In this paper we consider retransmission strategies for centralized cyclic polling-based systems over wireless channels subject to external interference. The considered strategies differ in the time when retransmissions for one particular node are carried out, and in the number of retransmissions that can be carried out for one node. We show experimentally and by simulation that two related strategies introduced in this paper, called the queueing-based strategies, significantly outperform the traditional strategy (in which all admissible trials towards one node are carried out subsequently) in terms of the average number of nodes that cannot be successfully served in a cycle. These performance gains are achieved without increasing the average total transmission effort.

Demodulating harmful nodes and diminishing the energy waste in sensor nodes can prolong the lifespan of wireless sensor networks (WSNs). In this study, a genetic algorithm (GA) and an adaptive neuro fuzzy inference system were used to diminish the energy waste of sensors. Weighted trust evaluation was applied to search for harmful nodes in the network to prolong the lifespan of WSNs. A low-energy adaptive clustering hierarchy method was used to analyze the results. It was discovered that searching for harmful nodes with GA-ANFIS using weighted trust evaluation significantly increased the lifespan of WSNs. For evaluation of the proposed method we used the mean of energy of all sensors against of the round, data packets received in base station, minimum energy versus rounds and number of alive sensors versus rounds. Also, in this paper we compared the proposed method results with LEACH, LEACH-DT, Random, SIF and GA-Fuzzy methods. As results the proposed method has high life time than other methods. A representation of the overall system was implemented using MATLAB software.

Deployment in a wireless sensor network is the first step towards constructing a network topology. There are existing techniques using the conventional approaches of geometry or simply random positions. However, with the advancement in Wireless sensor network technologies, it is now proved that efficient sensor node placement is essential for quality of service enhancements of such networks be it in terms of battery conservation, lifetime improvement, interference or simply efficient communications.

The Alessandrina Library was founded in 1667 by pope Alexander VII Chigi and is nowadays housed in the Campus of Sapienza University of Rome (Italy). Within its Ancient (mostly made of rag paper) and Modern (mostly made of contemporary paper) collections, it includes more than one million books produced from the XVI to the XXI century. In 2019, six thermo-hygrometers were deployed in its multi-storey repository to monitor temperature (T) and relative humidity (RH). Hourly T and RH data collected over 2 years allowed us to evaluate spatial and temporal thermohygrometric distributions and to carry out a comprehensive assessment of the climate-induced risks (mechanical, chemical, and biological deterioration mechanisms). Vertical temperature gradients associated with unstable conditions occurred in winter, resulting in upraising air flows up to the ceiling. The risky short-term RH fluctuations (EN 15757:2010) were determined to avoid mechanical stress in case of loans, relocation, and consultation. The Time Weighted Expected Lifetime (TWEL) index was used to evaluate the chemical risk for different paper-based collections as a function of their acidity and degree of polymerisation, also considering the typical response time of paper books to T and RH changes. The TWEL calculation estimated that the durability of acidic paper was around 300 years and highlighted that rag paper could be subject to cellulose hydrolysis only in summer and autumn, while contemporary paper was mostly at no risk. The risk of mould germination (Sedlbauer diagram) was possible on few days in Autumn, while the production of insect eggs (Brimblecombe empirical function) was favoured during approximately 42% of time over the year. In addition, illuminance and colorimetric measurements (performed on selected book covers) showed that light-sensitive objects could be exposed to the photodeterioration risk in the east-facing side of the repository. Although the investigation focussed on a specific case study, a similar approach could be effectively adapted to most library and archival repositories conserving paper-based collections.

In wireless sensor networks (WSNs), there are many factors, such as the reciprocal interference of wireless links, battlefield applications and nodes are exposed to poor physical protection medium. All of these result in the sensor nodes being more exposed to be attacked and compromised. Since sensors are normally energy limited in WSNs, it is constantly basic to conserve the node energy and expand the lifetime of the network. Besides, hierarchical clustering is deemed as one of the numerous ways to deal with the reduction of energy consumption of nodes and increasing the network lifetime in WSNs. This paper conspicuously introduces an efficient and secure malicious node detection model based on a hybrid clustering network for WSNs (ESMCH) which is based on a trusted mobile node. WSNs are still suffering from some attacks like Man-in-the-Middle Attack and Black hole Attack, so by using the ESMCH model, we can avoid these attacks. Finally, simulation results also indicate that the proposed model can increase the network lifetime, and achieve an efficient and secure clustering network.

The study of the microclimate is pivotal for the protection and conservation of cultural heritage. This paper describes specific procedures aimed at the deployment of microclimate sensors in spaces housing collections (e.g., museums) under different scenarios. The decision making involves a multidisciplinary discussion among museum manager, conservator and conservation scientist and implies five steps. Since the sensor's deployment depends on the number of available sensors, we have identified two possible circumstances: (a) artwork-related deployment (i.e., there are as many sensors as the number of artworks) and (b) artwork-envelope-related deployment (i.e., the number of available sensors is less than the number of artworks). The former circumstance is advisable when the artwork is often moved from a museum to another one. The latter circumstance is usually the case of permanent collections, and, according to the Museum Scenario (MS), the related procedures can be further subdivided into basic (MSI and MSII) and advanced (MSIII and MSIV). Advanced procedures are preferable over basic procedures when several time series of microclimate data have been collected for at least one calendar year in several sampling points. All these procedures make it possible to design where to deploy sensors both in the case of an initial deployment and of optimisation of already installed sensors.

In this research work, the egg laying radius of cuckoo search algorithm is used to create a cluster and then search for the optimum node based on multiobjective genetic algorithm with pareto ranking, so that the data can be forwarded to the sink.The primary focus is onthe two performance metrics parameters,one is the maximization of network lifetime and other is the minimization of delay. For maximizing the network lifetime parameter, the overlapped target sensing by many sensors is wastage of energy by two or more sensors, where the same task can be done by one sensor. To overcome this problem, the sequence set cover methodology is used.For minimization of delay parameter, the sleep-wake scheduling mechanism will be considered, but substantial delays are introduced as transmitting node needs to wait for its next-hop relay node to wake up. These delays can be taken care by developing any cast based packet forwarding schemes where individual node forwards a packet to the first neighboring node that wakes up among multiple candidate nodes. This any cast forwarding schemes minimizes the expected packet-delivery delays from the sensor nodes to the sink node. The introduced work will perform energy proficient routing with an objective to improve the network life, packet loss ratio and overall network throughput. The proposed algorithm was simulated in MATLAB and compared with LEACH algorithm. The results show that our proposed algorithm issuperiorfor prolonging the network lifetime, minimizing the packet loss and increasing the throughput.

With the growing increase of disasters worldwide, in terms of frequencies, intensities and unpredictability, there is currently a growing need for novel ubiquitous communication systems enabling public safety networks (PSN). Disaster operations require real-time and low latency data communication, in live video and audio streaming, as well as in integration with social networks. In this context, existing network infrastructures might be either damaged or overloaded. Thus, it is expected that wireless Body-to-Body Networks (B2Bs) could play a key role in setting-up tactical PSN deployments enabling effective and reliable disaster response and management. In addition to the challenges with radio technologies and their inter-operability, coexistence and energy consumption issues, routing is also an important and critical challenge for the PSN. In this paper we propose a novel wireless B2B routing protocol, called "Optimized Routing Approach for Critical and Emergency Networks (ORACE-Net)" to enhance routing performance

The study of the microclimate is pivotal for the protection and conservation of cultural heritage. This paper describes specific procedures aimed at the deployment of microclimate sensors in spaces housing collections (e.g., museums) under different scenarios. The decision making involves a multidisciplinary discussion among museum manager, conservator and conservation scientist and implies five steps. Since the sensor’s deployment depends on the number of available sensors, we have identified two possible circumstances: (a) artwork-related deployment (i.e., there are as many sensors as the number of artworks) and (b) artwork-envelope-related deployment (i.e., the number of available sensors is less than the number of artworks). The former circumstance is advisable when the artwork is often moved from a museum to another one. The latter circumstance is usually the case of permanent collections, and, according to the Museum Scenario (MS), the related procedures can be further subdivided into basic (MSI and MSII) and advanced (MSIII and MSIV). Advanced procedures are preferable over basic procedures when several time series of microclimate data have been collected for at least one calendar year in several sampling points. All these procedures make it possible to design where to deploy sensors both in the case of an initial deployment and of optimisation of already installed sensors.

Wireless sensor networks (WSNs) have drawn much research attention in recent years due to the superior performance in multiple applications, such as military and industrial monitoring, smart home, disaster restoration etc. In such applications, massive sensor nodes are randomly deployed and they remain static after the deployment, to fully cover the target sensing area. This will usually cause coverage redundancy or coverage hole problem. In order to effectively deploy sensors to cover whole area, we present a novel node deployment algorithm based on mobile sensors. First, sensor nodes are randomly deployed in target area, and they remain static or switch to the sleep mode after deployment. Second, we partition the network into grids and calculate the coverage rate of each grid. We select grids with lower coverage rate as candidate grids. Finally, we awake mobile sensors from sleep mode to fix coverage hole, particle swarm optimization (PSO) algorithm is used to calculate moving position of mobile sensors. Simulation results show that our algorithm can effectively improve the coverage rate of WSNs.

In this paper we proposed a multiobjective optimization model for wireless sensor networks (WSNs). The proposed model optimized several objectives, simultaneously. Indeed, by starting from a generic configuration we found new location for sensors, that the network have appropriate performance in terms of energy consumption and travelled distance. For the monotony of energy consumption and life time of sensors, the rate of energy consumption in each stage have been associated the previous stage. Through a series of calculations the behavior of the proposed model has been compared with other one-objective models.

Underwater Wireless Sensor Networks (UWSNs) are considered as wireless sensor networks whose main task is to sense underwater events and send information to the sink. This information becomes valuable when the exact location of the occurrence is known. Generally, underwater sensor nodes are not equipped with devices such as the Global Position System (GPS) with the purpose of reducing network costs. Therefore, finding the location of the nodes should be done using another exact method. In this paper, we intend to find the location of the underwater sensor nodes by introducing a new method based on the Cuckoo Optimization Algorithm (COA). We will compare the proposed method with the related methods in terms of the localization error rate and the number of nodes discovered. The results of the comparisons show that the proposed method can greatly reduce the error rate of the localization of the sensor nodes.

Surveillance has become a promising application area of wireless sensor networks (WSNs) where detecting and tracking an intruder as it moves through a sensor network is becoming an increasingly important challenge. This motivates us to investigate the impact of some factors such as the area geography and the intruders behaviours. For this, we present in this paper an efficient surveillance system that uses a WSN deployed inside a border area in order to detect an intruder at least N times from its entrance. The proposed surveillance system is examined under various environment characteristics. First, we focus on modeling the area into multi-thick lines architecture. Then, we provide a deployment method to detect the intruder from its entrance in an area without obstacles. Finally, we investigate the effect of the obstacles in the area and we provide a general surveillance system for detection purpose that considers different intruders behaviours.

Surveillance of personal locations with an untrusted system causes privacy breach/threats to the individuals those who are monitored. At this point, we propose a quality-aware locality monitoring system for wireless sensor networks. In this system, we outline a couple of in-network location algorithms such as resource-aware and quality aware algorithms, aimed to provide high-end location monitoring services for system users with outstanding quality, while protecting personal location privacy. Two techniques are based on the well-established (K) anonymity privacy concept; i.e., a person is unique among (K) individuals, to enable trusted sensor nodes to provide the average location information of monitored objects of the system. Each compound location is a part of a surveyed area/ location (L) along with the number of surveyed objects residing in (L), where (L) contains at least (K) persons. The resource aware algorithm minimizes communication cost and computational cost, while the quality aware algorithm maximizes the correctness of the aggregate location by lowering their monitored regions. To use the location information, to provide location monitoring service, we use a spatial histogram method, which calculates the distribution of the monitored objects created on the collected aggregate location information. Then, the calculated distribution provides location monitoring services by answering a series of questions. The system is evaluated through virtual experiments. The results indicate that this system provides an excellent location monitoring services while ensuring location privacy of the monitored persons.

In wireless sensor networks (WSNs), there are many factors, such as the reciprocal interference of wireless links, battlefield applications and nodes are exposed to poor physical protection medium. All of these result in the sensor nodes being more exposed to be attacked and compromised. Since sensors are normally energy limited in WSNs, it is constantly basic to conserve the node energy and expand the lifetime of the network. Besides, hierarchical clustering is deemed as one of the numerous ways to deal with the reduction of energy consumption of nodes and increasing the network lifetime in WSNs. This paper conspicuously introduces an efficient and secure malicious node detection model based on a hybrid clustering network for WSNs (ESMCH) which is based on a trusted mobile node. WSNs are still suffering from some attacks like Man-in-the-Middle Attack and Black hole Attack, so by using the ESMCH model, we can avoid these attacks. Finally, simulation results also indicate that the proposed model can increase the network lifetime, and achieve an efficient and secure clustering network.

The paper addresses the problem of efficiently monitoring environmental fields in a smart building by the use of a network of wireless noisy sensors that take discretelypredefined measurements at their locations through time. It is proposed that the indoor environmental fields are statistically modeled by spatio-temporal non-parametric Gaussian processes. The proposed models are able to effectively predict and estimate the indoor climate parameters at any time and at any locations of interest, which can be utilized to create timely maps of indoor environments. More importantly, the monitoring results are practically crucial for building management systems to efficiently control energy consumption and maximally improve human comfort in the building. The proposed approach was implemented in a real tested space in a university building, where the obtained results are highly promising.

The paper addresses the problem of efficiently deploying sensors in spatial environments, e.g. smart buildings, for the purpose of monitoring environmental phenomena. By modelling the environmental fields using spatio-temporal Gaussian processes, a new and efficient optimality criterion of minimizing prediction uncertainties is proposed to find the best sensor locations. Though the environmental processes spatially and temporally vary, the proposed approach of choosing sensor positions is not affected by time variations, which significantly reduces computational complexity of the optimization problem. The sensor deployment problem is then solved by a practically and feasibly polynomial algorithm, where its solutions are guaranteed. The proposed approaches were implemented in a real tested space in a university building, where the obtained results are highly promising.

The performance of a surveillance wireless sensor network is generally measured with its detection capability within a monitored zone. This capability is affected by various parameters such as the sensor count, the sensor range, the area width and the target mobility model. In real life examples it is observed that intruders prefer some favorite regions because of their geographical advantages within a monitored border area. These regions, which bounds the randomly chosen trespassing paths, are generally in close vicinity. In this paper, we introduce the notion of trespassers' favorite paths (TFP) and provide a tool that can be used to forecast the detection probability of a surveillance network in such a network with TFPs. The detection probability is reduced to the geometric line intersection problem using bijection and the boundary conditions of intruder trajectories for the border area and the favorite region are determined. The line intersection problem is solved using tools from the integral geometry and geometric probability. The effect of the favorable region on the detection quality under different conditions is calculated using probabilistic models. The accuracy of the proposed quality metric is validated by both analytical methods and simulation results. Furthermore, the importance of the intrusion model on the network performance is presented using realistic scenarios. It is shown that the existence of favorite paths has significant impact on the detection quality of the network. The proposed detection quality metric provides analytical tools suitable for both real life and simulation scenarios to the network designers to forecast and optimize the expected detection performance, and is computationally inexpensive compared to both simulation based and distributed quality measurements.

In recent years, wireless sensor networks (WSNs) have been considered one of the important topics for researchers due to their wide applications in our life. Several researches have been conducted to improve WSNs performance and solve their issues. One of these issues is the energy limitation in WSNs since the source of energy in most WSNs is the battery. Accordingly, various protocols and techniques have been proposed with the intention of reducing power consumption of WSNs and lengthen their lifetime. Cluster-oriented routing protocols are one of the most effective categories of these protocols. In this article, we consider a major issue affecting the performance of this category of protocols, which we call the intra/inter-cluster event-reporting problem (IICERP). We demonstrate that IICERP severely reduces the performance of a cluster-oriented routing protocol, so we suggest an effective Solution for IICERP (SIICERP). To assess SIICERP’s performance, comprehensive simulations wer...

Providing a global coverage in wireless sensor networks with least possible numbers of sensor nodes has an important effect on the quality of a service and collection of information. Following a local approach, a set of nodes are first randomly distributed in the desired region, and then are moved or new nodes added iteratively until the existing holes are covered. In this study, a local approach is proposed, which improves an existing (tree-based) method for detecting coverage holes and adding new nodes. The advantage of the proposed approach is that the coverage holes are covered using the existing overlapping nodes as much as possible. It means, when a coverage hole is detected, the existing nodes with an overlapping coverage are detected and moved in order to cover the hole; new nodes are added only when required. The experimental results show the efficiency of the proposed method in terms of the number of nodes as well as the number of iterations and time complexity.

Wireless networks data aggregation allows in-network processing, reduces packet transmission and data redundancy, and thus helps extend wireless sensor systems to the full duration of their lives. There have been many ways of dividing the network into clusters, collecting information from nodes and adding it to the base station, to extend wireless sensor network life. Certain cluster algorithms consider the residual energy of the nodes when selecting clusterheads and others regularly rotate the selection head of the cluster. However, we seldom investigate the network density or local distance. In this report we present an energy-efficient clustering algorithm that selects the best cluster heads of the system after dividing the network into clusters. The cluster head selection depends on the distance between the base station nodes and the remaining power of this approach.Each node's residual energy is compared to the node count. Our results show that the solution proposed more ef...

A central problem in environmental sensing and monitoring is to classify/label the hotspots in a large-scale environmental field. This paper presents a novel decentralized active robotic exploration (DARE) strategy for probabilistic classification/labeling of hotspots in a Gaussian process (GP)-based field. In contrast to existing state-of-the-art exploration strategies for learning environmental field maps, the time needed to solve the DARE strategy is independent of the map resolution and the number of robots, thus making it practical for in situ, real-time active sampling. Its exploration behavior exhibits an interesting formal trade-off between that of boundary tracking until the hotspot region boundary can be accurately predicted and wide-area coverage to find new boundaries in sparsely sampled areas to be tracked. We provide a theoretical guarantee on the active exploration performance of the DARE strategy: under reasonable conditional independence assumption, we prove that it...

In this paper, we describe about the performance of different mobility models on MMS Routing. MMS routing is the technique used for large wireless sensor networks where MMS indicates multiple mobile sinks. The mobility of sink is considered in random manner in the basic MMS routing. Here we show the effect of different mobility models in the sink‟s mobility. From the observations what we got from simulations we decide that wind mobility will be more energy efficient and it is good to be selected for wireless sensor network.

In this paper, we describe about the performance of different mobility models on MMS Routing. MMS routing is the technique used for large wireless sensor networks where MMS indicates multiple mobile sinks. The mobility of sink is considered in random manner in the basic MMS routing. Here we show the effect of different mobility models in the sink"s mobility. From the observations what we got from simulations we decide that wind mobility will be more energy efficient and it is good to be selected for wireless sensor network.

Wireless sensor networks have profound effects on many application fields like security management which need an immediate, fast and energy efficient route. In this paper, we define a QoS based network layer for security management of chemical products warehouse which can be classified as real-time and mission critical application. This application generate alert packets caused by unusual events which need a short end to end delay and low packet loss rate constraints. After each node compute his hop count and build his neighbors table in the initialization phase, packets can be routed to the sink. We use Random ReRouting protocol which dynamically transfers routine data packets to secondary paths in the network, while offering a fast track path with better QoS for the packets carrying unusual events data. We add an energy threshold for the routing protocol to control the energy consumption and we adapt the network topology changes by rerun the initialization phase when chemical units were added or removed from the warehouse. Analysis shows that the network layer is energy efficient and can meet the QoS constraints of unusual events packets.

Current greedy routing protocol (GFS) designed to find shortest path as a single routing objective. Considering only one routing objective is insufficient for the computation of a reliable rout, and can severely compromise network performance on the remaining overlooked objectives. This paper introduces the Dynamic and Reactive Reliability Estimation with Selective Metrics Mechanism (DRESM). The intended DRESM is constructed of two coherent techniques; the Fuzzy Logic Dynamic Nodes’ Reliability Estimation (FLDRE) and the Status Information Distribution and Outgoing Traffic Control Management (IDOTM). FLDRE introduce the notion of multi-criteria next relay node selection using fuzzy weighted logic multi-objectives. IDOTM provides the sender node with fresh information about its neighbour and control the outgoing traffic. The simulation results show that DRESM outperforms GFS in terms of packet delivery ratio, average end-to-end delay. Moreover, DRESM can find routs whose cost is clos...

Wireless sensors networks (WSNs) are traditionally composed of large number of tiny homogenous sensors nodes connected through a wireless network that gather data to be treated locally or relayed to the sink node through multi-hop wireless transmission. The low-energy adaptive clustering hierarchy (LEACH) protocol is one of the Famous protocols used in the wireless sensor networks (WSNs). The LEACH protocol in wireless sensor network suffers from many Bugs and many researchers proposed different methods to mitigate them. In this paper, we propose two ideas in a format for improving leach protocol. For Cluster head selection we used a Likely Attributable Function that in this function used from a factor. This factor that we called the informed selection factor helps to farther nodes not selection for cluster head. This significantly decreases the energy consumption and increases the lifetime of associated nodes. Simulation is conducted in using MATLAB results are analyzed for energy consumption.

The objective in this paper is to develop and demonstrate fhe capability of applying modeling and simulation techniques to maintain the network integrity of a packetswitched network. Here a new technique called performance trending is presented, in which key network parameters are monitored and network failure is predicted. A typical packet-switched network is simulated to veriw performance trending for network performance management.

Surveillance of personal locations with an untrusted system causes privacy breach/threats to the individuals those who are monitored. At this point, we propose a quality-aware locality monitoring system for wireless sensor networks. In this system, we outline a couple of in-network location algorithms such as resource-aware and quality aware algorithms, aimed to provide high-end location monitoring services for system users with outstanding quality, while protecting personal location privacy. Two techniques are based on the well-established (K) anonymity privacy concept; i.e., a person is unique among (K) individuals, to enable trusted sensor nodes to provide the average location information of monitored objects of the system. Each compound location is a part of a surveyed area/ location (L) along with the number of surveyed objects residing in (L), where (L) contains at least (K) persons. The resource aware algorithm minimizes communication cost and computational cost, while the qu...

Cooperative relaying has been proposed as a promising transmission technique that effectively creates spatial diversity through the cooperation among spatially distributed nodes. However, to achieve efficient communications while gaining full benefits from cooperation, more interactions at higher protocol layers, particularly the MAC (Medium Access Control) and network layers, are vitally required. This is ignored in most existing articles that mainly focus on physical (PHY)-layer relaying techniques. In this paper, we propose a novel crosslayer framework involving two levels of joint design-a MACnetwork cross-layer design for forwarder selection (or termed routing) and a MAC-PHY for relay selection-over symbol-wise varying channels. Based on location knowledge and contention processes, the proposed cross-layer protocol, CoopGeo, aims at providing an efficient, distributed approach to select next hops and optimal relays along a communication path. Simulation results demonstrate that CoopGeo not only operates properly with varying densities of nodes, but performs significantly better than the existing protocol BOSS in terms of packet error rate, transmission error probability, and saturated throughput.

In wireless sensor networks (WSNs), there are many factors, such as the reciprocal interference of wireless links, battlefield applications and nodes are exposed to poor physical protection medium. All of these result in the sensor nodes being more exposed to be attacked and compromised. Since sensors are normally energy limited in WSNs, it is constantly basic to conserve the node energy and expand the lifetime of the network. Besides, hierarchical clustering is deemed as one of the numerous ways to deal with the reduction of energy consumption of nodes and increasing the network lifetime in WSNs. This paper conspicuously introduces an efficient and secure malicious node detection model based on a hybrid clustering network for WSNs (ESMCH) which is based on a trusted mobile node. WSNs are still suffering from some attacks like Man-in-the-Middle Attack and Black hole Attack, so by using the ESMCH model, we can avoid these attacks. Finally, simulation results also indicate that the proposed model can increase the network lifetime, and achieve an efficient and secure clustering network.

In wireless sensor networks (WSNs), there are many factors, such as the reciprocal interference of wireless links, battlefield applications and nodes are exposed to poor physical protection medium. All of these result in the sensor nodes being more exposed to be attacked and compromised. Since sensors are normally energy limited in WSNs, it is constantly basic to conserve the node energy and expand the lifetime of the network. Besides, hierarchical clustering is deemed as one of the numerous ways to deal with the reduction of energy consumption of nodes and increasing the network lifetime in WSNs. This paper conspicuously introduces an efficient and secure malicious node detection model based on a hybrid clustering network for WSNs (ESMCH) which is based on a trusted mobile node. WSNs are still suffering from some attacks like Man-in-the-Middle Attack and Black hole Attack, so by using the ESMCH model, we can avoid these attacks. Finally, simulation results also indicate that the pr...

In wireless sensor networks (WSNs), there are many factors, such as the reciprocal interference of wireless links, battlefield applications and nodes are exposed to poor physical protection medium. All of these result in the sensor nodes being more exposed to be attacked and compromised. Since sensors are normally energy limited in WSNs, it is constantly basic to conserve the node energy and expand the lifetime of the network. Besides, hierarchical clustering is deemed as one of the numerous ways to deal with the reduction of energy consumption of nodes and increasing the network lifetime in WSNs. This paper conspicuously introduces an efficient and secure malicious node detection model based on a hybrid clustering network for WSNs (ESMCH) which is based on a trusted mobile node. WSNs are still suffering from some attacks like Man-in-the-Middle Attack and Black hole Attack, so by using the ESMCH model, we can avoid these attacks. Finally, simulation results also indicate that the proposed model can increase the network lifetime, and achieve an efficient and secure clustering network.

In wireless sensor networks (WSNs), there are many factors, such as the reciprocal interference of wireless links, battlefield applications and nodes are exposed to poor physical protection medium. All of these result in the sensor nodes being more exposed to be attacked and compromised. Since sensors are normally energy limited in WSNs, it is constantly basic to conserve the node energy and expand the lifetime of the network. Besides, hierarchical clustering is deemed as one of the numerous ways to deal with the reduction of energy consumption of nodes and increasing the network lifetime in WSNs. This paper conspicuously introduces an efficient and secure malicious node detection model based on a hybrid clustering network for WSNs (ESMCH) which is based on a trusted mobile node. WSNs are still suffering from some attacks like Man-in-the-Middle Attack and Black hole Attack, so by using the ESMCH model, we can avoid these attacks. Finally, simulation results also indicate that the proposed model can increase the network lifetime, and achieve an efficient and secure clustering network.

Wireless Sensor Networks are by nature highly dynamic and communication between sensors is completely ad hoc, especially when mobile devices are part of the setup. Numerous protocols and applications proposed for such networks operate on the assumption that knowledge of the neighborhood is a priori available to all nodes. As a result, WSN deployments need to use or implement from scratch a neighborhood discovery mechanism. In this work we present a new protocol based on adaptive periodic beacon exchanges. We totally avoid continuous beaconing by adjusting the rate of broadcasts using the concept of consistency over the understanding of neighborhood that nearby devices share. We propose, implement and evaluate our adaptive neighborhood discovery protocol over our experimental testbed and using large scale simulations. Our results indicate that the new protocol operates more efficiently than existing reference implementations while it provides valid information to applications that use it. Extensive performance evaluation indicates that it successfully reduces generated network traffic by 90% and increases network lifetime by 20% compared to existing mechanisms that rely on continuous beaconing.

Since we are not able to replace the battery in a wireless sensor networks (WSNs), the issues of energy and lifetime are the most important parameters. In asymmetrical networks, different sensors with various abilities are used. Super nodes, with higher power and wider range of communication in comparison with common sensors, are used to cause connectivity and transmit data to base stations in these networks. It is crucial to select the parameters of fit function and monitoring sensors optimally in a point covering network. In this paper, we utilized an algorithm to select monitoring sensors. The selection is done by using a novel algorithm that used by simulated annealing. This selection takes remained energy into consideration. This method increases lifetime, decreases and balances energy consumption as confirmed by simulation results.

The sensor networks, or more completely referred to as wireless sensor networks (WSNs), do not need a predetermined positioning in most cases. The sensor nodes (SNs) are randomly deployed in the SN's field, making a network. The deployment of the sensor nodes in the WSN depends on such an application, coverage area, and connectivity. The WSNs can deploy the SNs over a field with planning or deterministic topology, and self-organizing topologies. In the self-organizing topology, the SN's positions are formed randomly while the routes are unreliable and random. Because the random properties have many issues, it makes the SN's deployment becoming an essential issue. This paper aims to detect the most extensive area not covered by the sensor nodes in the WSN using a genetic algorithm (GA). Machine learning implementation has made a big through in widely computation use. The GA method could detect the sensor node-less area in the performance of the WSN with an accuracy of 91.67%.

The efficiency of sensor networks strongly depends on the routing protocol used. In this paper, we analyze three different types of routing protocols: LEACH, PEGASIS, and VGA. Sensor networks are simulated using Sensoria simulator. Several simulations are conducted to analyze the performance of these protocols including the power consumption and overall network performance. The simulation results, using same limited sensing range value, show that PEGASIS outperforms all other protocols while LEACH has better performance than VGA. Furthermore, the paper investigates the power consumption for all protocols. On the average, VGA has the worst power consumption when the sensing range is limited, while VGA is the best when the sensing range is increased.

There are various challenges faced by HR departments while recruiting and selecting the candidates for a particular job/profile. However in current trends, diverse tools and technologies are used to overcome those challenges in the organizations. Several challenges faced in recruitment and selection aretime consumption, lack of recruitment sources and less applicants applying for the job. This research is carried out to know the diversity of challenges faced by different organizations of different sectors while conducting recruitment and selection and prime tools and technologies used to manage those challenges. This research mainly focuses on whether organizations situated in and around Ahmedabad face challenges in recruitment and selection and if they take help of technology to manage those challenges. The research also enlightens us about diversity of challenges faced in different sectors and to know about particular challenges that are faced most in recruitment and selection. In this research, Human Resource Department of Various Organizations were contacted in order to accumulate the insights regarding challenges faced in their respective organizations and structured questionnaire is used as a compiling tool.

Monitoring important aquatic processes like harmful algal blooms is of increasing interest to public health,
ecosystem sustainability, marine biology, and aquaculture industry. This paper presents a novel approach to
spatiotemporal aquatic field reconstruction using inexpensive, low-power, mobile sensing platforms called
robotic fish. Robotic fish networks are a typical example of Cyber-Physical Systems where the design of
cyber components (sensing, communication, and information processing) must account for inherent physical
dynamics of the robots and the aquatic environment. Our approach features a rendezvous-based mobility
control scheme where robotic fish collaborate in the form of a swarm to sense the aquatic environment in a
series of carefully chosen rendezvous regions. We design a novel feedback control algorithm that maintains
the desirable level of wireless connectivity for a sensor swarm in the presence of significant environment and
system dynamics. Information-theoretic analysis is used to guide the selection of rendezvous regions so that
the spatiotemporal field reconstruction accuracy is maximized subject to the limited sensor mobility. The
effectiveness of our approach is validated via implementation on sensor hardware and extensive simulations
based on real data traces of water surface temperature field and on-water ZigBee wireless communication.

Monitoring important aquatic processes like harmful algal blooms is of increasing interest to public health, ecosystem sustainability, marine biology, and aquaculture industry. This paper presents a novel approach to spatiotemporal aquatic field reconstruction using inexpensive, low-power, mobile sensing platforms called robotic fish. Robotic fish networks are a typical example of Cyber-Physical Systems where the design of cyber components (sensing, communication, and information processing) must account for inherent physical dynamics of the robots and the aquatic environment. Our approach features a rendezvous-based mobility control scheme where robotic fish collaborate in the form of a swarm to sense the aquatic environment in a series of carefully chosen rendezvous regions. We design a novel feedback control algorithm that maintains the desirable level of wireless connectivity for a sensor swarm in the presence of significant environment and system dynamics. Information-theoretic analysis is used to guide the selection of rendezvous regions so that the spatiotemporal field reconstruction accuracy is maximized subject to the limited sensor mobility. The effectiveness of our approach is validated via implementation on sensor hardware and extensive simulations based on real data traces of water surface temperature field and on-water ZigBee wireless communication.

Networks of wireless sensors are increasingly exploited in crucial applications of monitoring spatially correlated environmental phenomena such as temperature, rainfall, soil ingredients, and air pollution. Such a network provides an ability to monitor and model the environmental fields even at unobserved locations. This requires estimating number of sensors and their sampling locations which minimize the uncertainty of predictions. Therefore, the aim of this thesis is to present novel, efficient and practically feasible approaches to efficiently sample the environments, so that the uncertainties at predicted locations are minimized. Gaussian process (GP) is utilized to statistically model the spatial field. This thesis includes both stationary wireless sensor networks (SWSNs) and mobile robotic wireless sensor networks (MRWSNs), and thus the issues are correspondingly formulated into \textit{sensor selection} and \textit{sensor placement} problems, respectively. In the first part of this thesis, a novel performance metric for the sensor selection in the SWSNs, named average root mean square error, which reflects the average uncertainty of each predicted location, is proposed. In order to minimize this NP-hard and combinatorial optimization, a simulated annealing based algorithm is proposed so that the sensor selection problem is effectively addressed. Particularly, when considering the sensor selection in constrained environments, \textit{e.g.} gas phase hydrogen sulphide in a sewage system, a modified GP with a new covariance function is developed. A new yet efficient mutual information maximization criterion is also presented to select the most informative gaseous sensor locations along the sewer system. Furthermore, the second part of this thesis introduces centralized and distributed methods for spatial prediction over time in the MRWSNs. For the purpose of finding the optimal sampling paths of the mobile wireless sensors to take the most informative observations at each time iteration, a sampling strategy is proposed based on minimizing the uncertainty at all unobserved locations. A novel and very efficient optimality criterion for the adaptive sampling problem is then presented so that the minimization can be addressed by a greedy algorithm in polynomial time. The solution is proven to be bounded; and computational time of the proposed algorithm is illustrated to be practically feasible for the resource-constrained MRWSNs. In order to enhance the issue of computational complexity, Gaussian Markov random field (GMRF) is utilized to model the spatial field exploiting sparsity of the precision matrix. A new GMRF optimality criterion for the adaptive navigation problem is also proposed such that computational complexity of the resulting optimization is deterministic even with increasing number of measurements. The algorithms were tested and verified with different spatial phenomena data sets with appealing results.

This paper addresses the problem of selecting the most informative sensor locations out of all possible sensing positions in predicting spatial phenomena by using a wireless sensor network. The spatial field is modelled by Gaussian Markov random fields (GMRF), where sparsity of the precision matrix enables the network to benefit from computation. A new spatial sensor selection criterion is proposed based on mutual information between random variables at selected locations and those at unselected locations and interested but unlikely sensor placed positions, which enhances resulting prediction. The GMRF based optimality criterion is then proven to be computationally and efficiently resolved, especially in a large-scale sensor network, by a polynomial time approximation algorithm. More importantly , with demonstrations of monotonicity and submodularity properties of the mutual information set function in the proposed selection criterion, our near-optimal solution is also guaranteed by at least within (1 − 1/e) of the optimal performance. The effectiveness of the proposed approach is compared and illustrated using two real-life large data sets with promising results.

This paper addresses the problem of driving robotic sensors for an energy-constrained mobile wireless network in efficiently monitoring and predicting spatial phenomena, under data locational errors. The paper first discusses how errors of mobile sensor locations affect estimating and predicting the spatial physical processes, given that spatial field to be monitored is modeled by a Gaussian process. It then proposes an optimality criterion for designing optimal sampling paths for the mobile robotic sensors given the localization uncertainties. Although the optimization problem is optimally intractable, it can be resolved by a polynomial approximation algorithm, which is proved to be practically feasible in an energy-constrained mobile sensor network. More importantly, near-optimal solutions of this navigation problem are guaranteed by a lower bound within 1−(1/e) of the optimum. The performance of the proposed approach is evaluated on simulated and real-world data sets, where impact of sensor location errors on the results is demonstrated by comparing the results with those obtained by using noiseless data locations.

The paper addresses the problem of efficiently deploying sensors in spatial environments, e.g. smart buildings , for the purpose of monitoring environmental phenomena. By modelling the environmental fields using spatio-temporal Gaussian processes, a new and efficient optimality criterion of minimizing prediction uncertainties is proposed to find the best sensor locations. Though the environmental processes spatially and temporally vary, the proposed approach of choosing sensor positions is not affected by time variations, which significantly reduces computational complexity of the optimization problem. The sensor deployment problem is then solved by a practically and feasibly polynomial algorithm, where its solutions are guaranteed. The proposed approaches were implemented in a real tested space in a university building, where the obtained results are highly promising.

The paper addresses the problem of efficiently monitoring environmental fields in a smart building by the use of a network of wireless noisy sensors that take discretely-predefined measurements at their locations through time. It is proposed that the indoor environmental fields are statistically modeled by spatio-temporal non-parametric Gaussian processes. The proposed models are able to effectively predict and estimate the indoor climate parameters at any time and at any locations of interest, which can be utilized to create timely maps of indoor environments. More importantly, the monitoring results are practically crucial for building management systems to efficiently control energy consumption and maximally improve human comfort in the building. The proposed approach was implemented in a real tested space in a university building, where the obtained results are highly promising.