From carbon nanotubes and silicate layers to graphene platelets for polymer nanocomposites

In spite of extensive studies conducted on carbon nanotubes and silicate layers for their polymer-based nanocomposites, the rise of graphene now provides a more promising candidate due to its exceptionally high mechanical performance and electrical and thermal conductivities. The present study developed a facile approach to fabricate epoxy–graphene nanocomposites by thermally expanding a commercial product followed by ultrasonication and solution-compounding with epoxy, and investigated their morphologies, mechanical properties, electrical conductivity and thermal mechanical behaviour. Graphene platelets (GnPs) of 3.5

Energy efficient wireless sensor network communications based on computational intelligent data fusion for environmental monitoring

The study presents a novel computational intelligence algorithm designed to optimise energy consumption in an environmental monitoring process: specifically, water level measurements in flooded areas. This algorithm aims to obtain a tradeoff between accuracy and power consumption. The implementation constitutes a data aggregation and fusion in itself. A harsh environment can make the direct measurement of flood levels a difficult task. This study proposes a flood level estimation, inferred through the measurement of other common environmental variables. The benefit of this algorithm is tested both with simulations and real experiments conducted in Donñana, a national park in southern Spain where flood level measurements have traditionally been done manually.Junta de Andalucía P07-TIC-0247

Solving Target Coverage Problem in Wireless Sensor Network Using Genetic Algorithm

The past few years have seen tremendous increase of interest in the field of wireless sensor network. These wireless sensor network comprise numerous small sensor nodes distributed in an area and collect specific data from that area. The nodes comprising a network are mostly battery driven and hence have a limited amount of energy. The target coverage deals with the surveillance of the area under consideration taking into account the energy constraint associated with nodes. In nutshell, the lifetime of the network is to be maximized while ensuring that all the targets are monitored. The approach of segregating the nodes into various covers is used such that each cover can monitor all the targets while other nodes in remaining covers are in sleep state. The covers are scheduled to operate in turn thereby ensuring that the targets are monitored all the time and the lifetime of the network is also maximized. The segregation method is based on Maximum Set Cover (MSC) problem which is transformed into Maximum Disjoint Set Cover problem (MDSC). This problem of finding Maximum Disjoint Set Cover falls under the category of NP-Complete problem. Hence, two heuristics based approach are discussed in this work; first Greedy Heuristic is implemented to be used as baseline. Then a Genetic Algorithm based approach is proposed that can solve this problem by evolutionary global search technique. The existing and proposed algorithms are coded and functionality verified using MATLAB R2010b and performance evaluation and comparisons are made in terms of number of sensors and sensing range

Implementation of coverage problem in wireless sensor network based on unit Disk model

Wireless sensor networks (WSNs) have a wide range of applicability in many industrial and civilian applications such as industrial process monitoring and control, environment and habitat monitoring, machine health monitoring, home automation, health care applications, nuclear reactor control, fire detection, object tracking and traffic control. A WSN consists of spatially distributed autonomous sensors those cooperatively monitor the physical or environmental conditions including temperature, sound, vibration, motion, pressure or pollutants. In sensor networks where the environment is needed to be remotely monitored, the data from the individual sensor nodes is sent to a central base station (often located far from the network), through which the end-user can access data. The number of sensor nodes in a Wireless Sensor Network can vary in the range of hundreds to thousands. Such a network may have many challenges like low energy consumption, functional independence, efficient distributed algorithms, transmission routes, coverage, synchronization, topology control, robustness and fault tolerance, cost of maintaining the sensors and lifetime of the network

Ambient Data Collection with Wireless Sensor Networks

One of the most important applications for wireless sensor networks (WSNs) is Data Collection, where sensing data arecollected at sensor nodes and forwarded to a central base station for further processing. Since using battery powers and wirelesscommunications, sensor nodes can be very small and easily attached at specified locations without disturbing surroundingenvironments. This makes WSN a competitive approach for data collection comparing with its wired counterpart. In this paper,we review recent advances in this research area. We first highlight the special features of data collection WSNs, by comparingwith wired data collection network and other WSN applications. With these features in mind, we then discuss issues and priorsolutions on the data gathering protocol design. Our discussion also covers different approaches for message dissemination, whichis a critical component for network control and management and greatly affects the overall performance of a data collectionWSNsystem

Deployment Policies to Reliably Maintain and Maximize Expected Coverage in a Wireless Sensor Network

The long-term operation of a wireless sensor network (WSN) requires the deployment of new sensors over time to restore any loss in network coverage and communication ability resulting from sensor failures. Over the course of several deployment actions it is important to consider the cost of maintaining the WSN in addition to any desired performance measures such as coverage, connectivity, or reliability. The resulting problem formulation is approached first through a time-based deployment model in which the network is restored to a fixed size at periodic time intervals. The network destruction spectrum (D-spectrum) has been introduced to estimate reliability and is more commonly applied to a static network, rather than a dynamic network where new sensors are deployed over time. We discuss how the D-spectrum can be incorporated to estimate reliability of a time-based deployment policy and the features that allow a wide range of deployment policies to be evaluated in an efficient manner. We next focus on a myopic condition-based deployment model where the network is observed at periodic time intervals and a fixed budget is available to deploy new sensors with each observation. With a limited budget available the model must address the complexity present in a dynamic network size in addition to a dynamic network topology, and the dependence of network reliability on the deployment action. We discuss how the D-spectrum can be applied to the myopic condition-based deployment problem, illustrating the value of the D-spectrum in a variety of maintenance settings beyond the traditional static network reliability problem. From the insight of the time-based and myopic condition-based deployment models, we present a Markov decision process (MDP) model for the condition-based deployment problem that captures the benefit of an action beyond the current time period. Methodology related to approximate dynamic programming (ADP) and approximate value iteration algorithms is presented to search for high quality deployment policies. In addition to the time-based and myopic condition-based deployment models, the MDP model is one of the few addressing the repeated deployment of new sensors as well as an emphasis on network reliability. For each model we discuss the relevant problem formulation, methodology to estimate network reliability, and demonstrate the performance in a range of test instances, comparing to alternative policies or models as appropriate. We conclude with a stochastic optimization model focused on a slightly different objective to maximize expected coverage with uncertainty in where a sensor lands in the network. We discuss a heuristic solution method that seeks to determine an optimal deployment of sensors, present results for a wide range of network sizes and explore the impact of sensor failures on both the model formulation and resulting deployment policy

Reliable cost-optimal deployment of wireless sensor networks

Wireless Sensor Networks (WSNs) technology is currently considered one of the key technologies for realizing the Internet of Things (IoT). Many of the important WSNs applications are critical in nature such that the failure of the WSN to carry out its required tasks can have serious detrimental effects. Consequently, guaranteeing that the WSN functions satisfactorily during its intended mission time, i.e. the WSN is reliable, is one of the fundamental requirements of the network deployment strategy. Achieving this requirement at a minimum deployment cost is particularly important for critical applications in which deployed SNs are equipped with expensive hardware. However, WSN reliability, defined in the traditional sense, especially in conjunction with minimizing the deployment cost, has not been considered as a deployment requirement in existing WSN deployment algorithms to the best of our knowledge. Addressing this major limitation is the central focus of this dissertation. We define the reliable cost-optimal WSN deployment as the one that has minimum deployment cost with a reliability level that meets or exceeds a minimum level specified by the targeted application. We coin the problem of finding such deployments, for a given set of application-specific parameters, the Minimum-Cost Reliability-Constrained Sensor Node Deployment Problem (MCRC-SDP). To accomplish the aim of the dissertation, we propose a novel WSN reliability metric which adopts a more accurate SN model than the model used in the existing metrics. The proposed reliability metric is used to formulate the MCRC-SDP as a constrained combinatorial optimization problem which we prove to be NP-Complete. Two heuristic WSN deployment optimization algorithms are then developed to find high quality solutions for the MCRC-SDP. Finally, we investigate the practical realization of the techniques that we developed as solutions of the MCRC-SDP. For this purpose, we discuss why existing WSN Topology Control Protocols (TCPs) are not suitable for managing such reliable cost-optimal deployments. Accordingly, we propose a practical TCP that is suitable for managing the sleep/active cycles of the redundant SNs in such deployments. Experimental results suggest that the proposed TCP\u27s overhead and network Time To Repair (TTR) are relatively low which demonstrates the applicability of our proposed deployment solution in practice

Energy efficient clustering and secure data aggregation in wireless sensor networks

Communication consumes the majority of a wireless sensor network\u27s limited energy. There are several ways to reduce the communication cost. Two approaches used in this work are clustering and in-network aggregation. The choice of a cluster head within each cluster is important because cluster heads use additional energy for their responsibilities and that burden needs to be carefully distributed. We introduce the energy constrained minimum dominating set (ECDS) to model the problem of optimally choosing cluster heads in the presence of energy constraints. We show its applicability to sensor networks and give an approximation algorithm of O(log n) for solving the ECDS problem. We propose a distributed algorithm for the constrained dominating set which runs in O(log n log [triangle]) rounds with high probability. We show experimentally that the distributed algorithm performs well in terms of energy usage, node lifetime, and clustering time and thus is very suitable for wireless sensor networks. Using aggregation in wireless sensor networks is another way to reduce the overall communication cost. However, changes in security are necessary when in- network aggregation is applied. Traditional end-to-end security is not suitable for use with in-network aggregation. A corrupted sensor has access to the intermediate data and can falsify results. Additively homomorphic encryption allows for aggregation of encrypted values, with the result being the same as the result as if unencrypted data were aggregated. Using public key cryptography, digital signatures can be used to achieve integrity. We propose a new algorithm using homomorphic encryption and additive digital signatures to achieve confidentiality, integrity and availability for in- network aggregation in wireless sensor networks. We prove that our digital signature algorithm which is based on Elliptic Curve Digital Signature Algorithm (ECDSA) is at least as secure as ECDSA. Even without in-network aggregation, security is a challenge in wireless sensor networks. In wireless sensor networks, not all messages need to be secured with the same level of encryption. We propose a new algorithm which provides adequate levels of security while providing much higher availablility [sic] than other security protocols. Our approach uses similar amounts of energy as a network without security --Abstract, page iv