This thesis explores the role mobile sensor's mobility model and how it affects surveillance system performance in term of area coverage and detection effectiveness. Several algorithms which are categorized into three types, namely, fully coordinated mobility, fully random mobility and emergent mobility models are discussed with their advantages and limitations. A multi-agent platform to organize mobile sensor nodes, control nodes and actor nodes was implemented. It demonstrated great flexibility and was favourable for its distributed, autonomous and cooperative problem-solving characters. Realistic scenarios which are based on three KheperaIII mobile robots and a model which mimics Waterloo regional airport were used to examine the implementation platform and evaluate performance of different mobility algorithms. Several practical issues related to software configurations and interface library were addressed as by-products. The experimental results from both simulation and real platform show that the area coverage and the detection effectiveness vary with applying different mobility models. Fully coordinated model's super efficiency comes with carefully task planning and high requirements of sensor navigational accuracy. Fully random model is the least efficient in area coverage and detection because of the repetitive searching of each sensor and among sensors. A self-organizing algorithm named anti-flocking which mimics solitary animal's social behaviour was first proposed. It works based on quite simple rules for achieving purposeful coordinated group action without explicit global control. Experimental results demonstrate its attractive target detection efficiency in term of both detection rate and detection time while providing desirable features such as scalability, robustness and adaptivity. From the simulation results, the detection rate of the anti-flocking model increases by 36.5% and average detection time decreases by 46.2% comparing with the fully random motion model. The real platform results also reflect the superior performance improvement.

The nature and complexity of the security threats faced by our society in recent years have made it clear that a smart pervasive surveillance system constitutes the most effective cure, as it presents a conducive framework for seamless interaction between preventative capabilities and investigative protocols. Applications such as wild-life preserve monitoring, natural disaster warnings, and facility surveillance tend to be characterized by large and remote geographic areas, requiring large numbers of unattended sensor nodes to cover the volume-of-interest. Such large unattended sensor networks add new challenges as well as complicate the system management problem. Such challenges can be in the form of distributed operation with collaborative decision making, adaptive performance, and energy-aware strategies, to name a few. To meet the challenges of these mission-critical applications, the sensor system must exhibit capabilities such as heterogeneous and self-organized behaviour, data and information fusion, and collaborative resources control and management. Sensor Management (SM) refers to the process that plans and controls the use of the sensor nodes in a manner that synergistically maximizes the success rate of the whole system in achieving the goals of its mission in assessing the situation in a timely, reliable, and accurate fashion. Managing heterogeneous sensors involves making decisions and compromises regarding alternate sensing strategies under time and resource availability constraints. As a result, the performance of the collective sensors dictates the performance of the entire system. Consequently, there is a need for an intelligent Sensor Management Framework (SMF) to drive the system performance. SMF provides a control system to manage and coordinate the use of sensing resources in a manner that maximizes the system success rate in achieving its goals. An SMF must handle an overwhelming amount of information collected, and adapt to the highly dynamic environments, in addition to network and system limitations. This thesis proposes a resource-aware and intelligent SMF for managing pervasive sensor systems in surveillance context. The proposed SMF considerably improves the process of information acquisition by coordinating the sensing resources in order to gather the most reliable data from a dynamic scene while operating under energy constraints. The proposed SMF addresses both the operation of the coordination paradigm, as well as, the local and collaborative decision making strategies. A conceptual analysis of the SM problem in a layered structure is discussed to introduce an open and flexible design framework based on the service-oriented architecture to provide a modular, reusable, and extendable framework for the proposed SMF solution. A novel sensor management architecture, called Extended Hybrid Architecture for Sensor Management (E-HASM), is proposed. E-HASM combines the operation of the holonic, federated, and market-based architectures in a complementary manner. Moreover, a team-theoretic formulation of Belief-Desire-Intention (BDI), that represent the E-HASM components, is proposed as a mechanism for effective energy-aware decision making to address the local sensor utility. Also, intelligent schemes that provide adaptive sensor operation to the changes in environment dynamics and sensor energy levels are designed to include adaptive sleep, active sensing, dynamic sensing range, adaptive multimodality, and constrained communication. Furthermore, surveillance systems usually operate under uncertainty in stochastic environment. Therefore, this research formulates the collaborative decision-making entities as Partially Observable Markov Decision Processes (POMDP). To increase the tracking quality and the level of the information reliability, cooperation between the sensors is adopted, which adds an extra dimension in the design of the proposed SMFs. The propose SMF is implemented using the Jadex platform and is compared to the popular centralized architecture. The results illustrate the operation of the proposed SMF outperforms in terms of tracking quality, detection rate, energy consumption, network lifetime, and scalability.

Wireless sensor network is an intelligent network system which has the self-monitoring functionality. It consists of many low-cost, low-power and small-sized sensor nodes that can communicate with each other to perform sensing and data processing. Acting as an important role in the system, network coverage usually has a huge effect on the system’s lifetime.In this thesis, particle swarm algorithm was used as a method to optimize the coverage in the coverage of wireless sensor network. A network coverage optimization strategy based on particle swarm optimization was proposed and MATLAB was used as a tool to apply the algorithm. The model used in this thesis is the probability sensing model and the coverage type is area coverage. Effectiveness of the algorithm is proved by simulation. The simulation of the algorithm suggests the optimal deployment can be determined if a certain parameter which in this thesis is the sensing range is given.

Shooter localization systems are used to detect and locate the origin of gunfire. A wireless sensor network is one possible implementation of such a system. A wireless sensor network is sensitive to synchronization errors. Localization techniques that rely on the timing will give less accurate or even useless results if the synchronization errors are too large. This thesis focuses on the influence of synchronization errors on the abilityto localize a shooter using a wireless sensor network. A localization algorithm is developed and implemented and the effect of synchronization errors is studied. The localization algorithm is evaluated using numerical experiments, simulations, and data from real gunshots collected at field trials. The results indicate that the developed localization algorithm is able to localizea shooter with quite good accuracy. However, the localization performance is to a high degree influenced by the geographical configuration of the network as well as the synchronization error. / Skottlokaliseringssystem används för att upptäcka och lokalisera ursprunget för avlossade skott. Ett trådlöst sensornätverk är ett sätt att utforma ett sådant system.Trådlösa sensornätverk är känsliga för synkroniseringsfel. Lokaliseringsmetoder som bygger på tidsobservationer kommer med för stora synkroniseringsfel ge dåliga eller helt felaktiga resultat. Detta examensarbete fokuserar på vilken inverkan synkroniseringsfel har på möjligheterna att lokalisera en skytt i ett trådlöst sensornätverk. En lokaliseringsalgoritm utvecklas och förmågan att korrekt lokalisera en skytt vid olika synkroniseringsfel undersöks. Lokaliseringsalgoritmen prövas med numeriska experiment, simuleringar och även för data från riktiga skottljud, insamlade vid fältförsök. Resultaten visar att lokaliseringsalgoritmen fungerar tillfredställande, men att lokaliseringsförmågan till stor del påverkas av synkroniseringsfel men även av sensornätverkets geografiska utseende.

Advances in the development of simple and cheap sensors give new possibilities with large sensor network deployments in monitoring and surveillance applications. Commonly, the sensor positions are not known, specifically, when sensors are randomly spread in a big area. Low cost sensors are constructed with as few components as possible to keep price and energy consumption down. This implies that self-positioning and communication capabilities are low. So the question: “How do you localize such sensors with good precision with a feasible approach?” is central. When no information is available a stable and robust localization algorithm is needed. In this thesis an acoustic sensor network is considered. With a movable acoustic source a well-defined and audible signal is transmitted at different spots. The sensors measure the time of arrival which corresponds to distance. A two-step sensor localization approach is applied that utilizes the estimated distances. A novel approach in the first step is presented to incorporate more measurements and gain more position information. Localization and ranging performance is evaluated with simulations and data collected at field trials. The results show that the novel approach attains higher accuracy and robustness.

This thesis explores the role mobile sensor's mobility model and how it affects surveillance system performance in term of area coverage and detection effectiveness. Several algorithms which are categorized into three types, namely, fully coordinated mobility, fully random mobility and emergent mobility models are discussed with their advantages and limitations. A multi-agent platform to organize mobile sensor nodes, control nodes and actor nodes was implemented. It demonstrated great flexibility and was favourable for its distributed, autonomous and cooperative problem-solving characters. Realistic scenarios which are based on three KheperaIII mobile robots and a model which mimics Waterloo regional airport were used to examine the implementation platform and evaluate performance of different mobility algorithms. Several practical issues related to software configurations and interface library were addressed as by-products. The experimental results from both simulation and real platform show that the area coverage and the detection effectiveness vary with applying different mobility models. Fully coordinated model's super efficiency comes with carefully task planning and high requirements of sensor navigational accuracy. Fully random model is the least efficient in area coverage and detection because of the repetitive searching of each sensor and among sensors. A self-organizing algorithm named anti-flocking which mimics solitary animal's social behaviour was first proposed. It works based on quite simple rules for achieving purposeful coordinated group action without explicit global control. Experimental results demonstrate its attractive target detection efficiency in term of both detection rate and detection time while providing desirable features such as scalability, robustness and adaptivity. From the simulation results, the detection rate of the anti-flocking model increases by 36.5% and average detection time decreases by 46.2% comparing with the fully random motion model. The real platform results also reflect the superior performance improvement.

In distributed systems, dependency among different computations of an application leads to a problem of deciding the locations of computations. Spatial requirements of a computation can be expressed in terms of spatial relationships with other computations. This research presents programming abstractions and language constructs which can be used for specifying spatial coordination requirements for distributed computations. A spatial coordination middleware has been implemented for satisfying spatial coordination requirements of systems implemented using the Actor model of concurrent computation. Our approach abstracts spatial requirements of concurrent computations and provides key programming primitives for specifying these requirements. We have also implemented a number of higher level spatial coordination primitives which can be translated into the basic primitives. Spatial requirements can be specified using these primitives and then the runtime system converts them into a constraint satisfaction problem and satisfies them. Our approach reduces the programming complexity and provides a middleware which separates spatial requirements from functional code and enables the application programmer to change spatial requirements at runtime without effecting application's functionality. We have identified some of the high level primitives and provided a mechanism to develop high level primitives on top of the basic primitives. This thesis presents the rationale, design, implementation, and evaluation of spatial coordination. By comparing programs written with and without our spatial coordination primitives, we show how spatial coordination enables a programmer to specify spatial requirements declaratively and simplify the programming task. Experimental results demonstrate the performance of the approach, as the number of constraints increases.

Advances in micro-sensor and wireless technology enable small but smart sensors to be developed for wide range environment-monitor applications. Since Sensor nodes only have limited power capacity and are difficult to recharge, how to prolong network lifetime is an important issue in wireless sensor networks design. Several topology control algorithms have been proposed to maintain the connectivity of wireless sensor network and reduce the energy consumption. Multi-hop Infrastructure Network Architecture (MINA) is a kind of Multi-layer Architecture for WSN topology, which utilizes hundred of sensors to transmit data to a sink. This architecture partitions sensor nodes into layers based on their distances (calculated by hop count) to BS. In this way, the node connected to more nodes will relay more data for other nodes. This make the node exhaust its battery power quickly and thus reduces the network lifetime. This study proposes an Efficient Energy Time-Slot Allocation (EETA) scheme which distributes time slots in accordance with the energy of neighbor nodes and the number of neighbor nodes. In addition, this work also devises an adaptive time slot size to reduce data packet drop in case when the node buffer is full. The simulation results show that the EETA performs better than the MINA in terms of network lifetime.

Wireless sensor networks (WSNs) have been widely used in many areas, such as early earthquake monitoring, building structure monitoring, and military surveillance. In this thesis, we focus on the wireless sensor network deployed in the battlefield, using random key predistribution scheme. Firstly we presented an analysis of the security impacts by node capture attack. Also, based on the node cloning attack, we proposed a new attack scheme, called compromised key redistribution attack, and discussed related attack scenarios. Besides, we have found out and conjectured that, when the overlapping factor of compromised key set is larger than 0.05, it is very possible (almost 90%) that the number of distinct compromised keys is 10.5% of the original key pool. This conjecture helps the adversary estimate the approximated size of original key pool by calculating the overlapping factor, thus calculate the probability that malicious nodes successfully establish connections with legitimate nodes.

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