Wireless Sensor Networks (WSNs) are now widely used for monitoring and controlling of systems where human intervention is not desirable or possible. Connected Dominating Sets (CDSs) based topology control in WSNs is one kind of hierarchical method to ensure sufficient coverage while reducing redundant connections in a relatively crowded network. Moreover, Minimum-sized Connected Dominating Set (MCDS) has become a well-known approach for constructing a Virtual Backbone (VB) to alleviate the broadcasting storm for efficient routing in WSNs extensively. However, no work considers the load-balance factor of CDSsin WSNs. In this dissertation, we first propose a new concept — the Load-Balanced CDS (LBCDS) and a new problem — the Load-Balanced Allocate Dominatee (LBAD) problem. Consequently, we propose a two-phase method to solve LBCDS and LBAD one by one and a one-phase Genetic Algorithm (GA) to solve the problems simultaneously. Secondly, since there is no performance ratio analysis in previously mentioned work, three problems are investigated and analyzed later. To be specific, the MinMax Degree Maximal Independent Set (MDMIS) problem, the Load-Balanced Virtual Backbone (LBVB) problem, and the MinMax Valid-Degree non Backbone node Allocation (MVBA) problem. Approximation algorithms and comprehensive theoretical analysis of the approximation factors are presented in the dissertation. On the other hand, in the current related literature, networks are deterministic where two nodes are assumed either connected or disconnected. In most real applications, however, there are many intermittently connected wireless links called lossy links, which only provide probabilistic connectivity. For WSNs with lossy links, we propose a Stochastic Network Model (SNM). Under this model, we measure the quality of CDSs using CDS reliability. In this dissertation, we construct an MCDS while its reliability is above a preset applicationspecified threshold, called Reliable MCDS (RMCDS). We propose a novel Genetic Algorithm (GA) with immigrant schemes called RMCDS-GA to solve the RMCDS problem. Finally, we apply the constructed LBCDS to a practical application under the realistic SNM model, namely data aggregation. To be specific, a new problem, Load-Balanced Data Aggregation Tree (LBDAT), is introduced finally. Our simulation results show that the proposed algorithms outperform the existing state-of-the-art approaches significantly.

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The use of wireless technology in the process industry is becoming increasingly important to obtain fast deployment at low cost. However, poor channel quality often leads to retransmissions, which are governed by Automatic Repeat Request (ARQ) schemes. While ARQ is a simple and useful tool to alleviate packet errors, it has considerable disadvantages: retransmissions lead to an increase in energy expenditure and latency. The use of Forward Error Correction (FEC) however offers several advantages. We consider a Hybrid-ARQ-Adaptive-FEC scheme (HAF) based on BCH codes and Channel State Information. This scheme is evaluated on AWGN and fading channels. It is shown that HAF offers significantly improved performance both in terms of energy efficiency and latency, as compared to ARQ.

This dissertation presents algorithms for achieving communication in the presence of adversarial attacks in large, decentralized, resource-constrained networks. We consider abstract single-hop communication settings where a set of senders 𝙎 wishes to directly communicate with a set of receivers 𝙍. These results are then extended to provide resource-efficient, multi-hop communication in wireless sensor networks (WSNs), where energy is critically scarce, and peer-to-peer (P2P) networks, where bandwidth and computational power are limited. Our algorithms are provably correct in the face of attacks by a computationally bounded adversary who seeks to disrupt communication between correct participants. The first major result in this dissertation addresses a general scenario involving single-hop communication in a time-slotted network where a single sender in 𝙎 wishes to transmit a message 𝘮 to a single receiver in 𝙍. The two players share a communication channel; however, there exists an adversary who aims to prevent the transmission of 𝘮 by periodically blocking this channel. There are costs to send, receive or block 𝘮 on the channel, and we ask: How much do the two players need to spend relative to the adversary in order to guarantee transmission of the message? This problem abstracts many types of conflict in information networks, and the associated costs represent an expenditure of network resources. We show that it is significantly more costly for the adversary to block 𝘮 than for the two players to achieve communication. Specifically, if the cost to send, receive and block 𝘮 in a slot are fixed constants, and the adversary spends a total of 𝘉 slots to try to block the message, then both the sender and receiver must be active in only O(𝘉ᵠ⁻¹ + 1) slots in expectation to transmit 𝘮, where φ = (1+ √5)/2 is the golden ratio. Surprisingly, this result holds even if (1) the value of 𝘉 is unknown to either player; (2) the adversary knows the algorithms of both players, but not their random bits; and (3) the adversary is able to launch attacks using total knowledge of past actions of both players. Finally, these results are applied to two concrete problems. First, we consider jamming attacks in WSNs and address the fundamental task of propagating 𝘮 from a single device to all others in a WSN in the presence of faults; this is the problem of reliable broadcast. Second, we examine how our algorithms can mitigate application-level distributed denial-of-service attacks in wired client-server scenarios. The second major result deals with a single-hop communication problem where now 𝙎 consists of multiple senders and there is still a single receiver who wishes to obtain a message 𝘮. However, many of the senders (strictly less than half) can be faulty, failing to send 𝘮 or sending incorrect messages. While the majority of the senders possess 𝘮, rather than listening to all of 𝙎 and majority filtering on the received data, we desire an algorithm that allows the single receiver to decide on 𝘮 in a more efficient manner. To investigate this scenario, we define and devise algorithms for a new data streaming problem called the Bad Santa problem which models the selection dilemma faced by the receiver. With our results for the Bad Santa problem, we consider the problem of energy-efficient reliable broadcast. All previous results on reliable broadcast require devices to spend significant time in the energy-expensive receiving state which is a critical problem in WSNs where devices are typically battery powered. In a popular WSN model, we give a reliable broadcast protocol that achieves optimal fault tolerance (i.e., tolerates the maximum number of faults in this WSN model) and improves over previous results by achieving an expected quadratic decrease in the cost to each device. For the case where the number of faults is within a (1-∊)-factor of the optimal fault tolerance, for any constant ∊>0, we give a reliable broadcast protocol that improves further by achieving an expected (roughly) exponential decrease in the cost to each device. The third and final major result of this dissertation addresses single-hop communication where 𝙎 and 𝙍 both consist of multiple peers that need to communicate in an attack-resistant P2P network. There are several analytical results on P2P networks that can tolerate an adversary who controls a large number of peers and uses them to disrupt network functionality. Unfortunately, in such systems, operations such as data retrieval and message sending incur significant communication costs. Here, we employ cryptographic techniques to define two protocols both of which are more efficient than existing solutions. For a network of 𝘯 peers, our first protocol is deterministic with O(log²𝘯) message complexity and our second protocol is randomized with expected O(log 𝘯) message complexity; both improve over all previous results. The hidden constants and setup costs for our protocols are small and no trusted third party is required. Finally, we present an analysis showing that our protocols are practical for deployment under significant churn and adversarial behaviour.

Wireless sensor networks offer the advantages of low cost, flexible measurement of phenomenon in a wide variety of applications, and easy deployment. Since sensor nodes are typically battery powered, energy efficiency is an important objective in designing sensor network algorithms. These algorithms are often application-specific, owing to the need to carefully optimize energy usage, and since deployments usually support a single or very few applications.<p> This thesis concerns applications in which the sensors monitor a continuous scalar field, such as temperature, and addresses the problem of determining the location of a contour line in this scalar field, in response to a query, and communicating this information to a designated sink node. An energy-efficient solution to this problem is proposed and evaluated. This solution includes new contour detection and query propagation algorithms, in-network-processing algorithms, and routing algorithms. Only a small fraction of network nodes may be adjacent to the desired contour line, and the contour detection and query propagation algorithms attempt to minimize processing and communication by the other network nodes. The in-network processing algorithms reduce communication volume through suppression, compression and aggregation techniques. Finally, the routing algorithms attempt to route the contour information to the sink as efficiently as possible, while meshing with the other algorithms. Simulation results show that the proposed algorithms yield significant improvements in data and message volumes compared to baseline models, while maintaining the integrity of the contour representation.

The objective of this thesis is to address the problem of energy conservation in wireless sensor networks by tackling two fundamental problems: topology control and data delivery. We first address energy-aware topology control taking into account throughput per unit energy as the primary metric of interest. Through both experimental observations and analysis, we show that the optimal topology is a function of traffic load in the network. We then propose a new topology control scheme, Adaptive Topology Control (ATC), which increases throughput per unit energy. Based on different coordinations among nodes, we proposed three ATC schemes: ATC-CP, ATC-IP, and ATC-MS. Through simulations, we show that three ATC schemes outperform static topology control schemes, and particularly the ATC-MS has the best performance under all environments. Secondly, we explore an energy-aware data delivery problem consisting of two sub-problems: downstream (from a sink to sensors) and upstream (from sensors to a sink) data delivery. Although we address the problems as two independent ones, we eventually solve those problems with two approaches: GARUDA-DN and GARUDA-UP which share a common structure, the minimum dominating set. For the downstream data delivery, we consider reliability as well as energy conservation since unreliable data delivery can increase energy consumption under high data loss rates. To reduce energy consumption and achieve robustness, we propose GARUDA-DN which is scalable to the network size, message characteristics, loss rate and the reliable delivery semantics. From ns2-based simulations, we show that GARUDA-DN performs significantly better than the basic schemes proposed thus far in terms of latency and energy consumption. For the upstream data delivery, we address an energy efficient aggregation scheme to gather correlated data with theoretical solutions: the shortest path tree (SPT), the minimum spanning tree (MST) and the Steiner minimum tree (SMT). To approximate the optimal solution in case of perfect correlation among data, we propose GARUDA-UP which combines the minimum dominating set (MDS) with SPT in order to aggregate correlated data. From discrete event simulations, we show that GARUDA-UP outperforms the SPT and closely approximates the centralized optimal solution, SMT, with less amount of overhead and in a decentralized fashion.

In this thesis, We propose two power saving strategy in wireless sensor networks with multi-packet reception and slotted ALOHA is as a systematic model. We concentrate on the case in which the packet arrival process is Bernoulli and the maximum queue is 1.This thesis first simulate results and to compare with the analytical results of pervious thesis. Traditional slotted ALOHA only have transmit and idle state. In this thesis, add a sleep state to decrease the energy consumption, and according to different strategy propose two different methods. This two methods decide to the sleep time and the retransmission probability to achieve the energy-efficient. At last we will use the simulation result to show the performance of our power saving strategy.

Recent advances in wireless communication and micro-electro-mechanical systems (MEMS) have led to the development of implementation of low-cost, low power, multifunctional sensor nodes. These sensor node are small in size and communicate untethered in short distances. The nodes in sensor networks have limited battery power and it is not feasible or possible to recharge or replace the batteries, therefore power consumption should be minimized so that overall network lifetime will be increased. In order to minimize power consumed during idle listening, some nodes, which can be considered redundant, can be put to sleep. In this thesis study, basic routing algorithms and duty cycle control algorithms for WSNs in the literature are studied. One of the duty cycle control algorithms, Role Alternating, Coverage Preserving, and Coordinated Sleep algorithm (RACP) is examined and simulated using the ns2 simulation environment. A novel duty cycle control algorithm, Sink Initiated Path Formation (SIPF) is proposed and compared to RACP in terms of sleep sensor ratio and time averaged coverage.

Wireless Sensor Networks (WSNs) have become a significant research area in recent years as they play an increasingly important role in bridging the gap between the physical and the virtual world. However, programming wireless sensor networks is extremely challenging. Middleware for WSNs supports the development of sensing-based applications and facilitates programming wireless sensor networks. Middleware is the software that sits in the middle of applications and operating system. There is a large amount of research on middleware development for WSNs. In this thesis, a first attempt is made to simulate mobile agents running on Agilla middleware using a software application. A Java application (Agilla Simulator) is developed and different agents corresponding to various functions are simulated. The performance of the network, namely the time it takes to execute the agents on the simulator is measured. The results of migration delay and reliability in the simulations of different agents are compared with those of the real world experiments. The comparison results presented in the study show that simulations produce results comparable to real life experiments.

Nowadays, low-cost and tiny sensors are started to be commonly used due to developing technology. Wireless sensor networks become the solution for a variety of applications such as military applications. For military applications, classification of a target in a battlefield plays an important role. Target classification can be done effectively by using wireless sensor networks. A wireless sensor node has the ability to sense the raw signal data in battlefield, extract the feature vectors from sensed signal and produce a local classification result using a classifier. Although only one sensor is enough to produce a classification result, decision fusion of the local classification results for the sensor nodes improves classification accuracy and loads lower computational burden on the sensor nodes. Decision fusion performance can also be improved by picking optimum sensor nodes for target classification. In this thesis, we propose fuzzy decision fusion methods for single target classification in wireless sensor networks. Our proposed fusion algorithms use fuzzy logic for selecting the appropriate sensor nodes to be used for classification. Our solutions provide better classification accuracy over some popular decision fusion algorithms. In addition to fusion algorithms, we present some techniques for feature vector size reduction on sensor nodes, and training set formation for classifiers.