We study the scheduling problem for data collection from sensor nodes to the sink node in wireless sensor networks, also referred to as the convergecast problem. The convergecast problem in general network topology has been proven to be NP-hard. In this paper, we propose our heuristic algorithm (finding the minimum scheduling time for convergecast (FMSTC)) for general network topology and evaluate the performance by simulation. The results of the simulation showed that the number of time slots to reach the sink node decreased with an increase in the power. We compared the performance of the proposed algorithm to the optimal time slots in a linear network topology. The proposed algorithm for convergecast in a general network topology has 2.27 times more time slots than that of a linear network topology. To the best of our knowledge, the proposed method is the first attempt to apply the optimal algorithm in a linear network topology to a general network topology.

The General Direction Routing Protocol (GDRP) is a Wireless Sensor Network (WSN) multi-path routing protocol which abstracts localization information (commonly GPS coordinates) into relative direction information in order to perform routing decisions. By generating relative direction information GDRP is able to operate with fewer precision requirements than other protocols. This abstraction also allows the integration of other emerging hardware-based localization techniques, for example, Beamforming Sensor Arrays. GDRP does not specifically address the next hop a packet should take, but instead specifies a direction it should travel. This direction abstraction allows for multiple paths to be taken through the network thus enhancing network robustness to node mobility and failures. This indirect addressing scheme also provides a solution to sensor node unique identification. GDRP is simulated in a custom simulator written in Java. This simulator supports interfaces for multiple protocols for layers 1, 2, 3, and 7 of the OSI model. For performance comparisons, GDRP is compared against multiple WSN routing protocols. GDRP operates with a significantly lower setup cost in terms of bytes transmitted and a lower setup latency for networks of varying sizes. It also demonstrates an exponentially lower routing cost when compared to another multi- path routing protocol due to a more efficient packet propagation in the network.

Les réseaux de capteurs sans fil sont une collection de nœuds non connectés qui sont installés pour la détection de certains phénomènes intéressants. Après avoir pris des mesures un capteur sans fil retransmet ces mesures à la station de base. La station de base collecte les données de tous les capteurs et les analyse. Pour économiser l’énergie il est souvent utilise de grouper les capteurs en clusters, chaque cluster ayant une tête de cluster qui communique avec la station de base. Au début, on commence par analyser la simulation des réseaux Zigbee où il y a quelques nœuds qui transmettent avec différentes puissances. Les résultats montrent que dans les réseaux de capteurs mobiles et hétérogènes et à cause du phénomène d’isolation des nœuds et du coût très élevé du routage et la maintenance, les performances sont moins bonnes que celles des réseaux homogènes. Le but principal de cette thèse est de faire une analyse empirique des réseaux de capteurs. A cause de leurs ressources limitées les réseaux de capteurs doivent faire face à plusieurs défis techniques. Beaucoup de protocoles fonctionnent très bien dans les simulateurs mais pas aussi bien en implémentation réelle. Par exemple, les capteurs déposés sur un objet élevé subissent moins d’atténuation que les autres capteurs placés sur le sol. Dans cette thèse, on montre qu’il y a un impact des liens asymétriques sur la topologie des réseaux de capteurs sans fil et que la qualité des liens (LQI) varie en permanence. On propose deux méthodes pour améliorer les performances des algorithmes basés sur la qualité des liens des réseaux de capteurs avec des liens asymétriques. Dans la première méthode, le réseau n’a pas d’autre choix que d’avoir des nœuds qui transmettent à des grandes distances et deviennent des clusters Head. Le nombre de clusters Head peut être donné par Matérn Hard-core process. Dans la seconde méthode, on propose HybridLQI qui améliore les algorithmes basés sur LQI sans ajouter des entêtes au réseau. Ensuite, on applique les approches de clustérisassions théoriques sur le réseau de capteurs réel. On applique Matérn Hard Core process et Max-Min heuristique de formation des clusters sur des nœuds «Tmote » dans des réseaux denses et des réseaux de faible densité. Les résultats empiriques ont montré la supériorité de Matérn sur Max-Min dans les besoins d’espace mémoire, la simplicité de l’implémentation et le nombre de messages de signalisation. Enfin, en utilisant les chaînes de Markov absorbantes et des mesures, on étudie les performances des techniques de la distribution de charge dans des réseaux de capteurs réels / Wireless sensor networks are the collection of wireless nodes that are deployed to monitor certain phenomena of interest. Once the node takes measurements it transmits to a base station over a wireless channel. The base station collects data from all the nodes and do further analysis. To save energy, it is often useful to build clusters, and the head of each cluster communicates with the base station. Initially, we do the simulation analysis of the Zigbee networks where few nodes are more powerful than the other nodes. The results show that in the mobile heterogeneous sensor networks, due to phenomenon orphaning and high cost of route discovery and maintenance, the performance of the network degrades with respect to the homogeneous network. The core of this thesis is to empirically analyze the sensor network. Due to its resource constraints, low power wireless sensor networks face several technical challenges. Many protocols work well on simulators but do not act as we expect in the actual deployments. For example, sensors physically placed at the top of the heap experience Free Space propagation model, while the sensors which are at the bottom of the heap have sharp fading channel characteristics. In this thesis, we show that impact of asymmetric links in the wireless sensor network topology and that link quality between sensors varies consistently. We propose two ways to improve the performance of Link Quality Indicator (LQI) based algorithms in the real asymmetric link sensor networks. In the first way, network has no choice but to have some sensors which can transmit over the larger distance and become cluster heads. The number of cluster heads can be given by Matérn Hard-Core process. In the second solution, we propose HybridLQI which improves the performance of LQI based algorithm without adding any overhead on the network. Later, we apply theoretical clustering approaches in sensor network to real world. We deploy Matérn Hard Core Process and Max-Min cluster Formation heuristic on real Tmote nodes in sparse as well as highly dense networks. Empirical results show clustering process based on Matérn Hard Core Process outperforms Max-Min Cluster formation in terms of the memory requirement, ease of implementation and number of messages needed for clustering. Finally, using Absorbing Markov chain and measurements we study the performance of load balancing techniques in real sensor networks.

Sensor networks including opportunistic networks of sensor-equipped smartphones as well as networks of embedded sensors can enable a wide range of applications including environmental monitoring, smart grids, intelligent transportation, and healthcare. In most real-world applications, to meet end-user requirements, the network operator needs to define and update the sensors' tasks dynamically, such as updating the parameters for sensor data collection or updating the sensors' code. Tasking sensor networks is necessary to reduce the effort in programming sensor networks. However, it is challenging due to dynamics and scale in terms of number of nodes, number of tasks, and sensing regions of the networks. In addition, tasking sensor networks must also be efficient in terms of bandwidth, latency, energy consumption, and memory usage. This dissertation identifies and addresses the problems of scalability and efficiency in tasking sensor networks. The first challenge in tasking sensor networks is to define a mechanism that represents multiple tasks and sensor groups efficiently taking into account the heterogeneity and mobility of sensors deployed over a large geographical region. Another challenge in tasking sensor networks in general, and embedded sensor networks in particular, is to design protocols that can not only efficiently disseminate tasks but also maintain a consistent view of the task to be performed among inherently unreliable and resource-limited sensors. We believe that a scalable and efficient tasking framework can greatly benefit the development and deployment of sensor network applications. Our thesis is that decoupling the task specification from task implementation using a spatial two-dimensional (2D) representation of a tasking region such as maps enables scalable, efficient, and resource-adaptive tasking over heterogeneous mobile sensor networks. In addition, reducing overhead in detecting inconsistencies across nodes enables scalable and efficient task dissemination and maintenance. We present the design, implementation, and evaluation of Zoom, a multiresolution tasking framework that efficiently encapsulates multiple tasks and sensor groups for sensor networks deployed in a large geographical region. The key ideas in Zoom are (i) decoupling task specification and task implementation to support heterogeneity, (ii) using maps for representing spatial sensor groups and tasks to scale with the number of sensor groups and sensing regions, and (iii) using image encoding techniques to reduce the map size and provide adaptation to sensor platforms with different resource capabilities. We present the design, implementation, and evaluation of our protocol, DHV, which efficiently disseminates task content and ensures that all nodes have up-to-date task content in sensor networks. It achieves this by minimizing both the redundant information in each message and the number of transmitted messages in the networks. DHV has been included in the official distribution of TinyOS, a popular operating system for embedded sensor networks. As sensor networks continue to develop, they will evolve from dedicated and single-purpose systems to open and multi-purpose large scale systems. Nodes in the network will be retasked frequently to support multiple applications and multiple users. We believe that this work is an important step in enabling seamless interaction between users and sensor networks and to make sensor networks more widely adopted.

Indiana University-Purdue University Indianapolis (IUPUI) / Wireless Sensor Networks (WSN) is being used in various applications including environmental monitoring, site inspection and military. WSN is a distributed network of sensor devices that can be used to monitor temperature, humidity, light and other important metrics. The software that runs on the sensor devices define how the device should operate. In real world WSN deployment, device software update is required to maintain optimal operation. In this thesis, we propose a novel idea of updating the software of the sensor nodes using a mobile device running on Android Operating System. Our implementation builds upon Mobile Deluge with few enhancement which is a method of re-programming WSN with laptop computer. We have evaluated our application performance by lab experiments and in real world deployments of WSN and found the application stable and battery efficient.

The sensor nodes in Wireless Sensor Networks (WSNs) and Mobile Sensor Networks (MSNs) can be prone to failures due to limited resources and/or the harsh environments where they are deployed. The network may be subject to partitioning if such failures are experienced by the cut-vertex nodes in the system. In case of partitioning, connectivity of the nodes in disjoint partitions with the sink node is disrupted. This not only affects the data delivery but also the possible cooperation and coordination of the nodes in handling certain events. To restore the connectivity of a partition with the rest of the network, network topology should be adjusted through either exploiting existing mobile nodes in the network or introducing additional relay nodes (RNs) to the network. However, both solutions pose certain challenges. In the former case, the mobility of the nodes requires significant energy consumption and thus the movement distance should be minimized. In addition, if the scope of the damage is too wide, determining the nodes to be relocated and their final locations is another challenge. In the latter case, determining the number of RNs and a self-configuring scheme for their movement destinations need to be tackled. In case of unavailability of sufficient RNs to provide connectivity with stable links to the whole network, another solution can be providing intermittent connectivity to the partitions by employing RNs as Mobile Data Collectors (MDCs). A mixed solution where some of the RNs are employed as MDCs and some as stationary RNs raises the challenge of determining the number of stationary RNs and identifying their locations, assigning MDCs to serve partitions uniformly in such a way that the tour lengths of MDCs are minimized and the load among the MDCs are balanced. In this dissertation, we address the connectivity restoration problem in partitioned WNSs and MSNs due to large scale damages. We present centralized and distributed approaches while considering four cases: 1. Minimizing the movement cost of the nodes while utilizing existing nodes in the network in case of the availability of the mobile nodes/actors. 2. Minimizing the number of relay nodes to be used and their movement cost in case of the lack of mobile nodes/actors in the network. 3. Maximizing the number of nodes served with a stable link while not exceeding the maximum tour length defined on MDCs when a mixed solution is required where some or all of the RNs are employed as MDCs. 4. Considering QoS constraints and rendezvous waiting time when multiple MDCs are in use. The effectiveness of all proposed approaches are validated through extensive simulation experiments.

Indiana University-Purdue University Indianapolis (IUPUI) / This study proposes and evaluates an average consensus scheme for wireless sensor networks. For this purpose, two communication error models, the fading signal error model and approximated fading signal error model, are introduced and incorporated into the proposed decentralized average consensus scheme. Also, a mathematical analysis is introduced to derive the approximated fading signal model from the fading signal model. Finally, differnt simulation scenarios are introduced and their results analyzed to evaluate the performance of the proposed scheme and its effectiveness in meeting the needs of wireless sensor networks.

In recent years, many strides have been achieved in the area of Wireless Sensor Networks (WSNs), which is leading to constant innovations in the types of applications that WSNs can support. Much advancement has also been achieved in the area of smart homes, enabling its occupants to manually and easily control their utility expenses. In this thesis, both areas of research will be colluded for a simple, yet critical application: efficient and economical comfort control in smart residential spaces. The goal is to design a central, modular energy consumption control system for residential spaces, which manages energy consumption in all aspects of a typical residence. This thesis is concerned with two facets of energy consumption in residences. The first facet is concerned with controlling when the heating, ventilating, and air conditioning unit (HVAC) operates for each room separately. This is in contrast to a typical HVAC system where comfort is provided across the floor as a whole. The second facet is concerned with controlling the lighting in each room so as to not exceed a certain input value. The communication network that supports the realization of these coveted goals is based on Zigbee interconnected sensor nodes which pour data unto a smart thermostat which does all the required calculations and activates the modules required for comfort control and energy management, if needed. A Java-based discrete event simulator is then written up to simulate a floor of a typical Canadian single-family dwelling. The simulation assumes error-less communication and proceeds to record certain room variables and the ongoing cost of operation periodically. These results from the simulator are compared to the results of the well known simulator, created by DesignBuilder, which describes typical home conditions. The conclusion from this analysis is that the Comfort Control and Energy Management Algorithms (CEMA) are feasible, and that their implementation incurs significant monetary savings.

Internet of Things (IoT) has drawn much attention in recent years. With IoT, physical world entities get connected through internet. IoT is used currently in various applications, such as environmental monitoring, control systems, farming, home automation, security and surveillance systems etc. The aim of this research is to design a low-cost, energy-efficient, reliable and scalable embedded application for the smart greenhouse monitoring system. The IoT based system designed in this thesis uses various sensors to measure the air and soil quality parameters in the greenhouse, and monitor real-time data online using web-server and mobile phone based applications. A ZigBee based wireless sensor network is implemented to transport various sensory data to the gateway. Among other contributions, the designed system develops a new routing algorithm by introducing a confirmed delivery of packets and re-routing features. We also introduced an efficient cost metric for making routing decisions within WSN using hops count, and simple bi-directional link quality estimator using PRR and current battery voltage of neighbor nodes. We also verified the stability of the system by conducting various performance tests. The system is equipped with data analytic functions for the online examination of the data. The designed system adopts event-based triggering and data aggregation methods to reduce the number of transmissions, and develops a new algorithm for such purpose. The web-server and mobile applications have user interface to display the output of the data analytic services, warning, control operations and give access to query data of the user's interest.

The existence of a coordinate system can often improve the routing in a wireless sensor network. While most coordinate systems correspond to the geometrical or geographical coordinates, in recent years researchers had proposed the use of virtual coordinates. Virtual coordinates depend only on the topology of the network as defined by the connectivity of the nodes, without requiring geographical information. The work in this thesis extends the use of virtual coordinates to scenarios where the wireless sensor network has a mobile sink. One reason to use a mobile sink is to distribute the energy consumption more evenly among the sensor nodes and thus extend the life-time of the network. We developed two algorithms, MS-DVCR and CU-DVCR which perform routing towards a mobile sink using virtual coordinates. In contrast to the baseline virtual coordinate routing MS-DVCR limits routing updates triggered by the sink movement to a local area around the sink. In contrast, CU-DVCR limits the route updates to a circular area on the boundary of the local area. We describe the design justification and the implementation of these algorithms. Using a set of experimental studies, we show that MS-DVCR and CU-DVCR achieve a lower energy consumption compared to the baseline virtual coordinate routing without any noticeable impact on routing performance. In addition, CU-DVCR provides a lower energy consumption than MS-DVCR for the case of a fast moving sink.