Silence-Based Communication

Communication complexity---the minimum amount of communication required---of computing a function of data held by several parties is studied. A communication model where silence is used to convey information is introduced. For this model the worst-case and average-case complexities of symmetric functions are studied. For binary-input functions the average- and worst-case complexities are determined and the protocols achieving them are described. For functions of non-binary inputs one-round communication, where each party is restricted to communicate in consecutive stages, is considered and the extra amount of communication required by one- over multi-round communication is analyzed. For the special case of ternary-input functions close lower and upper bounds on the worst-case one-round complexity are provided and protocols achieving them are described. Protocols achieving the average-case one-round complexity for ternary-input functions are also described. These protocols can be generalized to inputs of arbitrary size

A Distributed Solution for Visual Sensor Networks to Detect Targets in Crowds

Visual sensor networks (VSNs), a novel concept about fulfilling vision tasks by a network of collaborative visual sensors, has been attracting more and more attentions these days. This thesis introduces some pioneering research on developing a distributed algorithm for VSNs to detect targets in a cluttered scene. The algorithm is aimed to achieve excellent performances on both detection accuracy and energy efficiency. Based on a statistical model of the cluttered scene, the development starts with a centralized version where all the nodes send visual data to a central node and the central node invokes an iterative prioritization strategy (IPS) to make globally optimal detecting decisions. Although resulting in excellent detection accuracy, the centralized fashion causes poor performance on energy utilization. The algorithm is then transformed into a distributed version where the entire scene is partitioned into a Voronoi diagram and each node is only responsible for detecting targets inside its local polygon area. There are two challenges in realizing such a transformation. The first challenge is to design an energy-efficient method to exchange visual data among relevant nodes. A “back-projecting” strategy (BBR) is therefore created to tackle this challenge. Instead of sending request to nodes that have relevant data, the method initiates the data communication from source nodes. Each packet of visual data is then relayed towards the place where is located the target corresponding to the visual data. All the relevant data about the target will finally reach there and thereafter can be fused. This strategy enables the parallelism between transmitting visual data and integrating visual data for detection. With this parallelism, knowledge from partial detection results can be used to guide the transmission and therefore improve energy efficiency. The second challenge is to design a method to fuse decisions independently made by each node through small amount of mutual communication. A modified one-shot threshold strategy (MOTS) is proposed to tackle this challenge. By receiving small amount of data from related nodes, a local measure can be constructed to validate or invalidate local decisions. Compared with the centralized algorithm, this distributed algorithm demands less energy cost for a large-scale VSN and at same time sustains satisfactory detection accuracy. An experiment is presented in the end and the experimental results are analyzed

Self-Orienting Wireless Multimedia Sensor Networks for Maximizing Multimedia Coverage

Abstract—The performance of a wireless multimedia sensor network (WMSN) is tightly coupled with the pose of individual multimedia sensors. In particular, orientation of an individual multimedia sensor (direction of its sensing unit) is of great importance for the sensor network applications in order to capture the entire image of the field. In this paper, we study the problem of self-orientation in a wireless multimedia sensor network, that is finding the most beneficial pose of multimedia sensors to maximize multimedia coverage with occlusion-free viewpoints. We first propose a distributed algorithm to detect a node’s multimedia coverage and then determine its orientation, while minimizing the effect of occlusions and total overlapping regions in the sensing field. Our approach enables multimedia sensor nodes to compute their directional coverage, provisioning self-configurable sensor orientations in an efficient way. Simulations show that using distributed messaging and self-orientation having occlusion-free viewpoints significantly increase the multimedia coverage. I

Energy Efficient Designs for Collaborative Signal and Information Processing inWireless Sensor Networks

Collaborative signal and information processing (CSIP) plays an important role in the deployment of wireless sensor networks. Since each sensor has limited computing capability, constrained power usage, and limited sensing range, collaboration among sensor nodes is important in order to compensate for each other’s limitation as well as to improve the degree of fault tolerance. In order to support the execution of CSIP algorithms, distributed computing paradigm and clustering protocols, are needed, which are the major concentrations of this dissertation. In order to facilitate collaboration among sensor nodes, we present a mobile-agent computing paradigm, where instead of each sensor node sending local information to a processing center, as is typical in the client/server-based computing, the processing code is moved to the sensor nodes through mobile agents. We further conduct extensive performance evaluation versus the traditional client/server-based computing. Experimental results show that the mobile agent paradigm performs much better when the number of nodes is large while the client/server paradigm is advantageous when the number of nodes is small. Based on this result, we propose a hybrid computing paradigm that adopts different computing models within different clusters of sensor nodes. Either the client/server or the mobile agent paradigm can be employed within clusters or between clusters according to the different cluster configurations. This new computing paradigm can take full advantages of both client/server and mobile agent computing paradigms. Simulations show that the hybrid computing paradigm performs better than either the client/server or the mobile agent computing. The mobile agent itinerary has a significant impact on the overall performance of the sensor network. We thus formulate both the static mobile agent planning and the dynamic mobile agent planning as optimization problems. Based on the models, we present three itinerary planning algorithms. We have showed, through simulation, that the predictive dynamic itinerary performs the best under a wide range of conditions, thus making it particularly suitable for CSIP in wireless sensor networks. In order to facilitate the deployment of hybrid computing paradigm, we proposed a decentralized reactive clustering (DRC) protocol to cluster the sensor network in an energy-efficient way. The clustering process is only invoked by events occur in the sensor network. Nodes that do not detect the events are put into the sleep state to save energy. In addition, power control technique is used to minimize the transmission power needed. The advantages of DRC protocol are demonstrated through simulations

Distributed computing paradigms for collaborative signal and information processing in sensor networks

Abstract — In sensor networks, collaborative processing between multiple sensor nodes is essential in order to complement for each other’s sensing capability, tolerate faults, and provide reliable information. The client/server-based paradigm is typical for distributed processing. However, it is not the most efficient in the context of sensor networks. In this paper, we present a mobileagent-based paradigm to carry out collaborative processing, where instead of each sensor node sending local information to a processing center, as is typical in the client/server-based computing, the processing code is moved to the sensor nodes through mobile agents. This approach has great potential in providing energy-efficient and scalable collaborative processing with low latency. We design two metrics (execution time and energy consumption) and use simulation tools to quantitatively measure the performance of different computing models in collaborative processing. Experimental results show that the mobile agent paradigm performs much better when the number of nodes is large while the client/server paradigm is advantageous when the number of nodes is small. Based on this result, we develop a cluster-based hybrid computing paradigm to combine the advantages of both paradigms. We analyze two different scenarios in hybrid computing and simulation results show that there is always one scenario that performs better than either the client/server- or mobile-agent-based paradigm

Bandwidth-aware distributed ad-hoc grids in deployed wireless sensor networks

Nowadays, cost effective sensor networks can be deployed as a result of a plethora of recent engineering advances in wireless technology, storage miniaturisation, consolidated microprocessor design, and sensing technologies. Whilst sensor systems are becoming relatively cheap to deploy, two issues arise in their typical realisations: (i) the types of low-cost sensors often employed are capable of limited resolution and tend to produce noisy data; (ii) network bandwidths are relatively low and the energetic costs of using the radio to communicate are relatively high. To reduce the transmission of unnecessary data, there is a strong argument for performing local computation. However, this can require greater computational capacity than is available on a single low-power processor. Traditionally, such a problem has been addressed by using load balancing: fragmenting processes into tasks and distributing them amongst the least loaded nodes. However, the act of distributing tasks, and any subsequent communication between them, imposes a geographically defined load on the network. Because of the shared broadcast nature of the radio channels and MAC layers in common use, any communication within an area will be slowed by additional traffic, delaying the computation and reporting that relied on the availability of the network. In this dissertation, we explore the tradeoff between the distribution of computation, needed to enhance the computational abilities of networks of resource-constrained nodes, and the creation of network traffic that results from that distribution. We devise an application-independent distribution paradigm and a set of load distribution algorithms to allow computationally intensive applications to be collaboratively computed on resource-constrained devices. Then, we empirically investigate the effects of network traffic information on the distribution performance. We thus devise bandwidth-aware task offload mechanisms that, combining both nodes computational capabilities and local network conditions, investigate the impacts of making informed offload decisions on system performance. The highly deployment-specific nature of radio communication means that simulations that are capable of producing validated, high-quality, results are extremely hard to construct. Consequently, to produce meaningful results, our experiments have used empirical analysis based on a network of motes located at UCL, running a variety of I/O-bound, CPU-bound and mixed tasks. Using this setup, we have established that even relatively simple load sharing algorithms can improve performance over a range of different artificially generated scenarios, with more or less timely contextual information. In addition, we have taken a realistic application, based on location estimation, and implemented that across the same network with results that support the conclusions drawn from the artificially generated traffic